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GV05/shlomit_speech | GV05 | 2022-02-03T18:44:14Z | 302 | 0 | null | [
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Gauravadlakha1509/new_one | Gauravadlakha1509 | 2021-09-20T06:39:46Z | 302 | 0 | null | [
"region:us"
] | 2021-09-20T06:39:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | test | [
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HarrisDePerceptron/sv_corpora_parliament_processed | HarrisDePerceptron | 2022-02-06T22:08:17Z | 302 | 0 | null | [
"region:us"
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HarrisDePerceptron/ur_corpora_pib | HarrisDePerceptron | 2022-02-08T10:30:32Z | 302 | 0 | null | [
"region:us"
] | 2022-02-08T10:30:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Huertas97/autonlp-data-mami-semeval-20-21 | Huertas97 | 2021-10-21T08:03:52Z | 302 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
IFSTalfredoswald/MBTI | IFSTalfredoswald | 2021-10-25T10:40:02Z | 302 | 1 | null | [
"region:us"
] | 2021-10-25T10:40:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Intel/WEC-Eng | Intel | 2021-10-04T11:21:48Z | 302 | 0 | null | [
"region:us"
] | 2021-10-04T11:21:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # WEC-Eng
A large-scale dataset for cross-document event coreference extracted from English Wikipedia. </br>
- **Repository (Code for generating WEC):** https://github.com/AlonEirew/extract-wec
- **Paper:** https://aclanthology.org/2021.naacl-main.198/
### Languages
English
## Load Dataset
You can read in WEC-Eng files as follows (using the **huggingface_hub** library):
```json
from huggingface_hub import hf_hub_url, cached_download
import json
REPO_ID = "datasets/Intel/WEC-Eng"
splits_files = ["Dev_Event_gold_mentions_validated.json",
"Test_Event_gold_mentions_validated.json",
"Train_Event_gold_mentions.json"]
wec_eng = list()
for split_file in splits_files:
wec_eng.append(json.load(open(cached_download(
hf_hub_url(REPO_ID, split_file)), "r")))
```
## Dataset Structure
### Data Splits
- **Final version of the English CD event coreference dataset**<br>
- Train - Train_Event_gold_mentions.json
- Dev - Dev_Event_gold_mentions_validated.json
- Test - Test_Event_gold_mentions_validated.json
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Clusters | 7,042 | 233 | 322 |
| Event Mentions | 40,529 | 1250 | 1,893 |
- **The non (within clusters) controlled version of the dataset (lexical diversity)**<br>
- All (experimental) - All_Event_gold_mentions_unfiltered.json
### Data Instances
```json
{
"coref_chain": 2293469,
"coref_link": "Family Values Tour 1998",
"doc_id": "House of Pain",
"mention_context": [
"From",
"then",
"on",
",",
"the",
"members",
"continued",
"their"
],
"mention_head": "Tour",
"mention_head_lemma": "Tour",
"mention_head_pos": "PROPN",
"mention_id": "108172",
"mention_index": 1,
"mention_ner": "UNK",
"mention_type": 8,
"predicted_coref_chain": null,
"sent_id": 2,
"tokens_number": [
50,
51,
52,
53
],
"tokens_str": "Family Values Tour 1998",
"topic_id": -1
}
```
### Data Fields
|Field|Value Type|Value|
|---|:---:|---|
|coref_chain|Numeric|Coreference chain/cluster ID|
|coref_link|String|Coreference link wikipeida page/article title|
|doc_id|String|Mention page/article title|
|mention_context|List[String]|Tokenized mention paragraph (including mention)|
|mention_head|String|Mention span head token|
|mention_head_lemma|String|Mention span head token lemma|
|mention_head_pos|String|Mention span head token POS|
|mention_id|String|Mention id|
|mention_index|Numeric|Mention index in json file|
|mention_ner|String|Mention NER|
|tokens_number|List[Numeric]|Mentions tokens ids within the context|
|tokens_str|String|Mention span text|
|topic_id|Ignore|Ignore|
|mention_type|Ignore|Ignore|
|predicted_coref_chain|Ignore|Ignore|
|sent_id|Ignore|Ignore|
## Citation
```
@inproceedings{eirew-etal-2021-wec,
title = "{WEC}: Deriving a Large-scale Cross-document Event Coreference dataset from {W}ikipedia",
author = "Eirew, Alon and
Cattan, Arie and
Dagan, Ido",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.198",
doi = "10.18653/v1/2021.naacl-main.198",
pages = "2498--2510",
abstract = "Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing. However, existing corpora for this task are scarce and relatively small, while annotating only modest-size clusters of documents belonging to the same topic. To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a large-scale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics. We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages. To set baseline results, we develop an algorithm that adapts components of state-of-the-art models for within-document coreference resolution to the cross-document setting. Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.",
}
```
## License
We provide the following data sets under a <a href="https://creativecommons.org/licenses/by-sa/3.0/deed.en_US">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License
## Contact
If you have any questions please create a Github issue at https://github.com/AlonEirew/extract-wec. | [
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JIsanan/war-ceb-wikipedia | JIsanan | 2021-10-24T01:48:04Z | 302 | 0 | null | [
"region:us"
] | 2021-10-24T01:48:04Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | annotations_creators: []
language_creators:
- found
languages:
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licenses: []
multilinguality:
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pretty_name: Waray Cebu Wikipedia
size_categories:
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Jack0508/TED2020_kor | Jack0508 | 2021-11-07T19:45:21Z | 302 | 1 | null | [
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Jack0508/TED2020_vi | Jack0508 | 2021-11-07T19:42:16Z | 302 | 0 | null | [
"region:us"
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Jack0508/TED2020vi_kor | Jack0508 | 2021-11-07T17:21:21Z | 302 | 0 | null | [
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LysandreJik/demo3 | LysandreJik | 2021-09-25T19:58:09Z | 302 | 0 | null | [
"region:us"
] | 2021-09-25T19:58:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/push-to-hub | LysandreJik | 2021-10-07T22:59:15Z | 302 | 0 | null | [
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LysandreJik/pushe-to-hub | LysandreJik | 2021-10-07T23:23:34Z | 302 | 0 | null | [
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LysandreJik/pushed-to-hub | LysandreJik | 2021-10-07T22:33:54Z | 302 | 0 | null | [
"region:us"
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LysandreJik/pushedd-to-hub | LysandreJik | 2021-10-07T23:25:03Z | 302 | 0 | null | [
"region:us"
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LysandreJik/random_repo | LysandreJik | 2021-11-23T13:04:30Z | 302 | 0 | null | [
"region:us"
] | 2021-11-23T13:04:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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LysandreJik/test-16336477963335 | LysandreJik | 2021-10-07T23:03:17Z | 302 | 0 | null | [
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LysandreJik/test-16336478042515 | LysandreJik | 2021-10-07T23:03:25Z | 302 | 0 | null | [
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LysandreJik/test-16336479967338 | LysandreJik | 2021-10-07T23:06:38Z | 302 | 0 | null | [
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LysandreJik/test-16336480189315 | LysandreJik | 2021-10-07T23:07:00Z | 302 | 0 | null | [
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LysandreJik/test-16336486877862 | LysandreJik | 2021-10-07T23:18:09Z | 302 | 0 | null | [
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LysandreJik/test-16340052972855 | LysandreJik | 2021-10-12T02:21:38Z | 302 | 0 | null | [
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LysandreJik/test-16344349332219 | LysandreJik | 2021-10-17T01:42:14Z | 302 | 0 | null | [
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LysandreJik/test-16344349440339 | LysandreJik | 2021-10-17T01:42:25Z | 302 | 0 | null | [
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LysandreJik/test-16344360501144 | LysandreJik | 2021-10-17T02:00:51Z | 302 | 0 | null | [
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LysandreJik/test-16344362261113 | LysandreJik | 2021-10-17T02:03:47Z | 302 | 0 | null | [
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LysandreJik/test-16344362895458 | LysandreJik | 2021-10-17T02:04:51Z | 302 | 0 | null | [
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LysandreJik/test-16344364230608 | LysandreJik | 2021-10-17T02:07:04Z | 302 | 0 | null | [
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LysandreJik/test-16344364547167 | LysandreJik | 2021-10-17T02:07:36Z | 302 | 0 | null | [
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LysandreJik/test-16344367190179 | LysandreJik | 2021-10-17T02:12:00Z | 302 | 0 | null | [
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JonathanSum/github-issues | JonathanSum | 2021-12-28T12:36:48Z | 302 | 0 | null | [
"region:us"
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KTH/martin | KTH | 2022-01-31T11:59:02Z | 302 | 0 | null | [
"region:us"
] | 2022-01-31T11:59:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | hello
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Karavet/ARPA-Armenian-Paraphrase-Corpus | Karavet | 2022-10-21T16:04:07Z | 302 | 0 | null | [
"multilinguality:monolingual",
"language:hy",
"arxiv:2009.12615",
"region:us"
] | 2022-10-21T16:04:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- hy
task_categories: [paraphrase, paraphrase detection]
multilinguality: [monolingual]
task_ids: [paraphrase, paraphrase detection]
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Structure](#dataset-structure)
- [Dataset Evaluation](#dataset-evaluation)
- [Additional Information](#additional-information)
## Dataset Description
We provide sentential paraphrase detection train, test datasets as well as BERT-based models for the Armenian language.
### Dataset Summary
The sentences in the dataset are taken from [Hetq](https://hetq.am/) and [Panarmenian](http://www.panarmenian.net/) news articles. To generate paraphrase for the sentences, we used back translation from Armenian to English. We repeated the step twice, after which the generated paraphrases were manually reviewed. Invalid sentences were filtered out, while the rest were labelled as either paraphrase, near paraphrase or non-paraphrase. Test examples were reviewed by 3 different annotators. In addition, to increase the number of non-paraphrase pairs, we padded the dataset with automatically generated negative examples, including pairs of consecutive sentences and random pairs.
## Dataset Structure
Each row consists of 2 sentences and their label. This sentences were labelled as either paraphrase, near paraphrase or non-paraphrase (with 1, 0, -1 labels respectively). The sentences are divided into train and test sets.
|Number of examples|Total|Paraphrase|Non-paraphrase (near paraphrase)|
|:-- | :---: | :---: | :---: |
|Train | 4233 |1339 |2683 (211) |
|Test | 1682 |1021 |448 (213) |
### Dataset Evaluation
We finetuned Multilingual BERT on several training sets, including the proposed ARPA dataset, and evaluated their performance on our test set. During training and
evaluation, near paraphrase and non-paraphrase pairs were combined into one class. The results are provided below:
|BERT Model | Train set | F1 | Acc. |
|:-- | :---: | :---: | :---: |
|Multilingual BERT | ARPA train set| 84.27| 78.06|
|Multilingual BERT | Paraphraser.ru train set machine-translated into Armenian | 83.81 | 77.09 |
|Multilingual BERT | MRPC train set machine-translated into Armenian | 80.07 | 69.87 |
|Multilingual BERT | All of the above combined | 84 |77.6 |
#### Additional Information
The model trained on ARPA is available for use, and can be downloaded using this [link](https://drive.google.com/uc?id=14owW5kkZ1JiNa6P-676e-QIj8m8i5e_8).
For more details about the models and dataset construction, refer to the [paper](https://arxiv.org/pdf/2009.12615).
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Karavet/ILUR-news-text-classification-corpus | Karavet | 2022-10-21T16:06:12Z | 302 | 0 | null | [
"task_categories:text-classification",
"multilinguality:monolingual",
"language:hy",
"license:apache-2.0",
"region:us"
] | 2022-10-21T16:06:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- hy
task_categories: [news-classification, text-classification]
multilinguality: [monolingual]
task_ids: [news-classification, text-classification]
license:
- apache-2.0
---
## Table of Contents
- [Table of Contents](#table-of-contents)
- [News Texts Dataset](#news-texts-dataset)
## News Texts Dataset
We release a dataset of over 12000 news articles from [iLur.am](http://www.ilur.am/), categorized into 7 classes: sport, politics, weather, economy, accidents, art, society. The articles are split into train (2242k tokens) and test sets (425k tokens).
For more details, refer to the [paper](https://arxiv.org/ftp/arxiv/papers/1906/1906.03134.pdf). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PaulLerner/triviaqa_for_viquae | PaulLerner | 2022-08-02T08:24:26Z | 302 | 0 | null | [
"region:us"
] | 2022-08-02T08:24:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
Get the original dataset there: http://nlp.cs.washington.edu/triviaqa/ (or via HF: https://huggingface.co/datasets/trivia_qa) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Sunbird/salt-dataset | Sunbird | 2022-03-28T13:04:56Z | 302 | 3 | null | [
"region:us"
] | 2022-03-28T13:04:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | A parallel text corpus, **SALT -- (Sunbird African Language Translation Dataset)**, was created for five Ugandan languages (Luganda,
Runyankore, Acholi, Lugbara and Ateso) and various methods were explored to train and evaluate translation models. | [
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VadorMazer/skyrimdialogstest | VadorMazer | 2021-11-19T00:32:06Z | 302 | 0 | null | [
"region:us"
] | 2021-11-19T00:32:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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abidlabs/test-image-13 | abidlabs | 2022-01-19T19:33:09Z | 302 | 1 | null | [
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abidlabs/test-image-classifier-dataset | abidlabs | 2021-12-23T19:41:31Z | 302 | 0 | null | [
"region:us"
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anuragshas/sl_opus100_processed | anuragshas | 2022-02-02T13:54:18Z | 302 | 0 | null | [
"region:us"
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benjaminbeilharz/empathetic_dialogues_for_lm | benjaminbeilharz | 2022-02-23T15:49:12Z | 302 | 1 | null | [
"region:us"
] | 2022-02-23T15:49:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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cbrew475/hwu66 | cbrew475 | 2022-02-22T18:18:36Z | 302 | 0 | null | [
"region:us"
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cestwc/adapted-paranmt5m | cestwc | 2021-12-15T11:37:07Z | 302 | 3 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cgarciae/point-cloud-mnist | cgarciae | 2021-10-31T23:09:55Z | 302 | 2 | null | [
"region:us"
] | 2021-10-31T23:09:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Point CLoud MNIST
A point cloud version of the original MNIST.
## Getting Started
```python
import matplotlib.pyplot as plt
import numpy as np
from datasets import load_dataset
# load dataset
dataset = load_dataset("cgarciae/point-cloud-mnist")
dataset.set_format("np")
# get numpy arrays
X_train = dataset["train"]["points"]
y_train = dataset["train"]["label"]
X_test = dataset["test"]["points"]
y_test = dataset["test"]["label"]
# plot some training samples
figure = plt.figure(figsize=(10, 10))
for i in range(3):
for j in range(3):
k = 3 * i + j
plt.subplot(3, 3, k + 1)
idx = np.random.randint(0, len(X_train))
plt.title(f"{y_train[idx]}")
plt.scatter(X_train[idx, :, 0], X_train[idx, :, 1])
plt.show()
```
## Format
* `points`: `(batch, point, 3)` array of uint8.
* `label`: `(batch, 1)` array of uint8.
Where `point` is the number of points in the point cloud. Points have no order and were shuffled when creating the data. Each point has the structure `[x, y, v]` where:
* `x`: is the x coordinate of the point in the image.
* `y`: is the y coordinate of the point in the image.
* `v`: is the value of the pixel at the point in the image.
Samples are padded with `0`s such that `point = 351` since its the largest number of non-zero pixels per image in the original dataset. You can tell apart padding point because they are the only ones where `v = 0`.
Here is the distribution of non-zero pixels in the MNIST:
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gigant/romanian_speech_synthesis_0_8_1 | gigant | 2022-10-24T17:38:35Z | 302 | 2 | null | [
"task_categories:automatic-speech-recognition",
"language:ro",
"license:unknown",
"region:us"
] | 2022-10-24T17:38:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- ro
license:
- unknown
size_categories:
ro:
- 1K<n<10K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: Romanian Speech Synthesis
---
## Dataset Description
- **Homepage:** https://romaniantts.com/rssdb/
- **Paper:** https://www.sciencedirect.com/science/article/abs/pii/S0167639310002074
### Dataset Summary
The Romanian speech synthesis (RSS) corpus was recorded in a hemianechoic chamber (anechoic walls and ceiling; floor partially anechoic) at the University of Edinburgh. We used three high quality studio microphones: a Neumann u89i (large diaphragm condenser), a Sennheiser MKH 800 (small diaphragm condenser with very wide bandwidth) and a DPA 4035 (headset-mounted condenser). Although the current release includes only speech data recorded via Sennheiser MKH 800, we may release speech data recorded via other microphones in the future. All recordings were made at 96 kHz sampling frequency and 24 bits per sample, then downsampled to 48 kHz sampling frequency. For recording, downsampling and bit rate conversion, we used ProTools HD hardware and software. We conducted 8 sessions over the course of a month, recording about 500 sentences in each session. At the start of each session, the speaker listened to a previously recorded sample, in order to attain a similar voice quality and intonation.
### Languages
Romanian
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
The train split consists of 3180 audio clips and the related sentences.
The test split consists of 536 audio clips and the related sentences.
### Citation Information
```
@article{Stan2011442,
author = {Adriana Stan and Junichi Yamagishi and Simon King and
Matthew Aylett},
title = {The {R}omanian speech synthesis ({RSS}) corpus:
Building a high quality {HMM}-based speech synthesis
system using a high sampling rate},
journal = {Speech Communication},
volume = {53},
number = {3},
pages = {442--450},
note = {},
abstract = {This paper first introduces a newly-recorded high
quality Romanian speech corpus designed for speech
synthesis, called ''RSS'', along with Romanian
front-end text processing modules and HMM-based
synthetic voices built from the corpus. All of these
are now freely available for academic use in order to
promote Romanian speech technology research. The RSS
corpus comprises 3500 training sentences and 500 test
sentences uttered by a female speaker and was recorded
using multiple microphones at 96 kHz sampling
frequency in a hemianechoic chamber. The details of the
new Romanian text processor we have developed are also
given. Using the database, we then revisit some basic
configuration choices of speech synthesis, such as
waveform sampling frequency and auditory frequency
warping scale, with the aim of improving speaker
similarity, which is an acknowledged weakness of
current HMM-based speech synthesisers. As we
demonstrate using perceptual tests, these configuration
choices can make substantial differences to the quality
of the synthetic speech. Contrary to common practice in
automatic speech recognition, higher waveform sampling
frequencies can offer enhanced feature extraction and
improved speaker similarity for HMM-based speech
synthesis.},
doi = {10.1016/j.specom.2010.12.002},
issn = {0167-6393},
keywords = {Speech synthesis, HTS, Romanian, HMMs, Sampling
frequency, Auditory scale},
url = {http://www.sciencedirect.com/science/article/pii/S0167639310002074},
year = 2011
}
```
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. | [
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yhavinga/mc4_nl_cleaned | yhavinga | 2022-12-16T09:24:34Z | 302 | 7 | mc4 | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"multilinguality:en-nl",
"source_datasets:extended",
"language:nl",
"language:en",
"license:odc-by",
"arxiv:1910.10683",
"region:us"
] | 2022-12-16T09:24:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- nl
- en
license:
- odc-by
multilinguality:
- monolingual
- en-nl
size_categories:
micro:
- 120k
tiny:
- 1M<n<10M
small:
- 10M<n<100M
medium:
- 10M<n<100M
large:
- 10M<n<100M
full:
- 100M<n<1B
source_datasets:
- extended
task_categories:
- text-generation
task_ids:
- language-modeling
paperswithcode_id: mc4
pretty_name: mC4_nl_cleaned
---
# Dataset Card for Clean Dutch mC4
## Table of Contents
- [Dataset Card for Clean](#dataset-card-for-mc4)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Preprocessing](#preprocessing)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Original Homepage:** [HF Hub](https://huggingface.co/datasets/allenai/c4)
- **Paper:** [ArXiv](https://arxiv.org/abs/1910.10683)
### Dataset Summary
A cleaned version (151GB) of the Dutch part (277GB) of the C4 multilingual dataset (mC4).
While this dataset is monolingual, it is possible to download `en-nl` interleaved data, see the Dataset Config section below.
Based on the [Common Crawl dataset](https://commoncrawl.org).
The original version was prepared by [AllenAI](https://allenai.org/), hosted at the address [https://huggingface.co/datasets/allenai/c4](https://huggingface.co/datasets/allenai/c4).
### Preprocessing
The Dutch portion of mC4 was cleaned in a similar fashion as the English cleaned C4 version.
See [GitLab](https://gitlab.com/yhavinga/c4nlpreproc) for details.
In summary, the preprocessing procedure includes:
- Removing documents containing words from a selection of the [Dutch and English List of Dirty Naught Obscene and Otherwise Bad Words](https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words).
- Removing sentences containing:
- Less than 3 words.
- A word longer than 250 characters.
- An end symbol not matching end-of-sentence punctuation.
- Strings associated to javascript code (e.g. `{`), lorem ipsum, policy information in Dutch or English.
- Removing documents (after sentence filtering):
- Containing less than 5 sentences.
- Containing less than 500 or more than 50'000 characters.
- Not identified as prevalently Dutch by the `LangDetect` package.
Using parallel processing with 96 CPU cores on a TPUv3 via Google Cloud to perform the complete clean of all the original Dutch
shards of mC4 (1024 of ~220Mb train, 4 of ~24Mb validation) required roughly 10 hours due to the demanding steps of sentence
tokenization and language detection. The total size of compressed `.json.gz` files is roughly halved after the procedure.
## Dataset Structure
### Data Instances
An example from the dataset:
```
{
'timestamp': '2019-02-22T15:37:25Z',
'url': 'https://ondernemingen.bnpparibasfortis.be/nl/artikel?n=vijf-gouden-tips-voor-succesvol-zaken-doen-met-japan',
'text': 'Japanse bedrijven zijn niet alleen hondstrouw aan hun leveranciers , ze betalen ook nog eens erg stipt. Alleen is het niet zo makkelijk er een voet tussen de deur te krijgen. Met de volgende tips hebt u alvast een streepje voor.\nIn Japan draait alles om vertrouwen. Neem voldoende tijd om een relatie op te bouwen.Aarzel niet om tijdig een lokale vertrouwenspersoon in te schakelen.\nJapan is een erg competitieve markt.Kwaliteit en prijs zijn erg belangrijk, u zult dus het beste van uzelf moeten geven. Gelukkig is de beloning groot. Japanse zakenlui zijn loyaal en betalen stipt!\nJapanners houden er eigenzinnige eisen op na. Kom dus niet aanzetten met uw standaardproducten voor de Europese markt. Zo moet een producent van diepvriesfrieten bijvoorbeeld perfect identieke frietjes kunnen leveren in mini- verpakkingen. Het goede nieuws is dat Japanners voor kwaliteit graag diep in hun buidel tasten.\nEn u dacht dat Europa lijdt aan reglementitis? Japanners kennen er ook wat van. Tal van voorschriften zeggen wat je wel en niet mag doen. Gelukkig zijn de regels helder geformuleerd.\nHet gebruik van het Engels is niet echt ingeburgerd in Japan. Betrek een tolk bij uw onderhandelingen en zorg voor correcte vertalingen van handleidingen of softwareprogramma’s.'
}
```
### Data Fields
The data contains the following fields:
- `url`: url of the source as a string
- `text`: text content as a string
- `timestamp`: timestamp of extraction as a string
### Data Configs
To build mC4, the original authors used [CLD3](https://github.com/google/cld3) to identify over 100 languages.
For Dutch, the whole corpus of scraped text was divided in `1032` jsonl files, `1024` for training following
the naming style `c4-nl-cleaned.tfrecord-0XXXX-of-01024.json.gz` and 4 for validation following the
naming style `c4-nl-cleaned.tfrecord-0000X-of-00004.json.gz`. The full set of pre-processed files takes roughly 208GB of disk space to download with Git LFS.
For ease of use under different storage capacities, the following incremental configs are available: (note: files on disk are compressed)
| config | train size (docs, words, download + preproc disk space) | validation size |
|:-------|--------------------------------------------------------:|----------------:|
| micro | 125k docs, 23M words (<1GB) | 16k docs |
| tiny | 6M docs, 2B words (6 GB + 15 GB) | 16k docs |
| small | 15M docs, 6B words (14 GB + 36 GB) | 16k docs |
| medium | 31M docs, 12B words (28 GB + 72 GB) | 32k docs |
| large | 47M docs, 19B words (42 GB + 108 GB) | 48k docs |
| full | 64M docs, 25B words (58 GB + 148 GB) | 64k docs |
For each config above there also exists a config `<name>_en_nl` that interleaves `nl` and `en` examples from the cleaned
`en` variant of C4.
You can load any config like this:
```python
from datasets import load_dataset
datasets = load_dataset('yhavinga/mc4_nl_cleaned', 'tiny', streaming=True)
print(datasets)
```
This will print
```
DatasetDict({
train: Dataset({
features: ['text', 'timestamp', 'url'],
num_rows: 6303893
})
validation: Dataset({
features: ['text', 'timestamp', 'url'],
num_rows: 16189
})
})
```
Since the configs are quite large, you may want to traverse them using the streaming mode available starting from — Datasets v1.9.0:
```python
from datasets import load_dataset
mc4_nl_full_stream = load_dataset('yhavinga/mc4_nl_cleaned', "full", split='train', streaming=True)
print(next(iter(mc4_nl_full_stream))) # Prints the example presented above
```
## Dataset Creation
Refer to the original paper for more considerations regarding the choice of sources and the scraping process for creating `mC4`.
## Considerations for Using the Data
### Social Impact of Dataset
With more than 151GB (58GB compressed) of cleaned Dutch text and more than 23B estimated words, this is by far the largest available cleaned corpus for the Dutch language.
The second largest dataset available is [OSCAR](https://oscar-corpus.com/), which is only 39GB in size for its deduplicated variant, and contains vulgarity.
Using this corpus for training language models with adequate computational resources will allow researchers to reach parity with the performances observed for the English language.
This can in turn have important repercussions for the development of commercial language technology applications for the Dutch language.
### Discussion of Biases
Despite the cleaning procedure aimed at removing vulgarity and profanity, it must be considered that model trained on this scraped corpus will
inevitably reflect biases present in blog articles and comments on the Internet.
This makes the corpus especially interesting in the context of studying data biases and how to limit their impacts.
## Additional Information
### Licensing Information
AllenAI are releasing this dataset under the terms of ODC-BY. By using this, you are also bound by the Common Crawl terms of use in respect of the content contained in the dataset.
### Citation Information
```
@article{2019t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {arXiv e-prints},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.10683},
}
```
### Contributions
Thanks to [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com), [@dirkgr](https://github.com/dirkgr) and [@lhoestq](https://github.com/lhoestq) for
providing the `cleaned_it_mc4` example that shows how upload a dataset to the Huggingface hub.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lucadiliello/newsqa | lucadiliello | 2023-06-06T08:36:25Z | 302 | 3 | null | [
"region:us"
] | 2023-06-06T08:36:25Z | 2023-02-25T18:03:41.000Z | 2023-02-25T18:03:41 | ---
dataset_info:
features:
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence: string
- name: key
dtype: string
- name: labels
list:
- name: end
sequence: int64
- name: start
sequence: int64
splits:
- name: train
num_bytes: 234711053
num_examples: 74160
- name: validation
num_bytes: 13234782
num_examples: 4212
download_size: 31328809
dataset_size: 247945835
---
# Dataset Card for "newsqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mutual_friends | null | 2022-11-18T21:31:53Z | 301 | 2 | mutualfriends | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:dialogue-modeling",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1704.07130",
"region:us"
] | 2022-11-18T21:31:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- dialogue-modeling
paperswithcode_id: mutualfriends
pretty_name: MutualFriends
dataset_info:
features:
- name: uuid
dtype: string
- name: scenario_uuid
dtype: string
- name: scenario_alphas
sequence: float32
- name: scenario_attributes
sequence:
- name: unique
dtype: bool_
- name: value_type
dtype: string
- name: name
dtype: string
- name: scenario_kbs
sequence:
sequence:
sequence:
sequence: string
- name: agents
struct:
- name: '1'
dtype: string
- name: '0'
dtype: string
- name: outcome_reward
dtype: int32
- name: events
struct:
- name: actions
sequence: string
- name: start_times
sequence: float32
- name: data_messages
sequence: string
- name: data_selects
sequence:
- name: attributes
sequence: string
- name: values
sequence: string
- name: agents
sequence: int32
- name: times
sequence: float32
config_name: plain_text
splits:
- name: train
num_bytes: 26979472
num_examples: 8967
- name: test
num_bytes: 3327158
num_examples: 1107
- name: validation
num_bytes: 3267881
num_examples: 1083
download_size: 41274578
dataset_size: 33574511
---
# Dataset Card for MutualFriends
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [COCOA](https://stanfordnlp.github.io/cocoa/)
- **Repository:** [Github repository](https://github.com/stanfordnlp/cocoa)
- **Paper:** [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (ACL 2017)](https://arxiv.org/abs/1704.07130)
- **Codalab**: [Codalab](https://worksheets.codalab.org/worksheets/0xc757f29f5c794e5eb7bfa8ca9c945573/)
### Dataset Summary
Our goal is to build systems that collaborate with people by exchanging information through natural language and reasoning over structured knowledge base. In the MutualFriend task, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend.
### Supported Tasks and Leaderboards
We consider two agents, each with a private knowledge base of items, who must communicate their knowledge to achieve a common goal. Specifically, we designed the MutualFriends task (see the figure below). Each agent has a list of friends with attributes like school, major etc. They must chat with each other to find the unique mutual friend.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An example looks like this.
```
{
'uuid': 'C_423324a5fff045d78bef75a6f295a3f4'
'scenario_uuid': 'S_hvmRM4YNJd55ecT5',
'scenario_alphas': [0.30000001192092896, 1.0, 1.0],
'scenario_attributes': {
'name': ['School', 'Company', 'Location Preference'],
'unique': [False, False, False],
'value_type': ['school', 'company', 'loc_pref']
},
'scenario_kbs': [
[
[['School', 'Company', 'Location Preference'], ['Longwood College', 'Alton Steel', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Leonard Green & Partners', 'indoor']],
[['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'Crazy Eddie', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Rhodes College', "Tully's Coffee", 'indoor']],
[['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'AMR Corporation', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['New Mexico Highlands University', 'The Hartford Financial Services Group', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Sacred Heart University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Babson College', 'The Hartford Financial Services Group', 'indoor']]
],
[
[['School', 'Company', 'Location Preference'], ['National Technological University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Leonard Green & Partners', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Data Resources Inc.', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Salisbury State University', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['Fairmont State College', 'Molycorp', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'Molycorp', 'indoor']],
[['School', 'Company', 'Location Preference'], ['University of South Carolina - Aiken', 'STX', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['National Technological University', 'STX', 'outdoor']],
[['School', 'Company', 'Location Preference'], ['Johnson C. Smith University', 'Rockstar Games', 'indoor']]
]
],
'agents': {
'0': 'human',
'1': 'human'
},
'outcome_reward': 1,
'events': {
'actions': ['message', 'message', 'message', 'message', 'select', 'select'],
'agents': [1, 1, 0, 0, 1, 0],
'data_messages': ['Hello', 'Do you know anyone who works at Molycorp?', 'Hi. All of my friends like the indoors.', 'Ihave two friends that work at Molycorp. They went to Salisbury and Sacred Heart.', '', ''],
'data_selects': {
'attributes': [
[], [], [], [], ['School', 'Company', 'Location Preference'], ['School', 'Company', 'Location Preference']
],
'values': [
[], [], [], [], ['Salisbury State University', 'Molycorp', 'indoor'], ['Salisbury State University', 'Molycorp', 'indoor']
]
},
'start_times': [-1.0, -1.0, -1.0, -1.0, -1.0, -1.0],
'times': [1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0, 1480737280.0]
},
}
```
### Data Fields
- `uuid`: example id.
- `scenario_uuid`: scenario id.
- `scenario_alphas`: scenario alphas.
- `scenario_attributes`: all the attributes considered in the scenario. The dictionaries are liniearized: to reconstruct the dictionary of attribute i-th, one should extract the i-th elements of `unique`, `value_type` and `name`.
- `unique`: bool.
- `value_type`: code/type of the attribute.
- `name`: name of the attribute.
- `scenario_kbs`: descriptions of the persons present in the two users' databases. List of two (one for each user in the dialogue). `scenario_kbs[i]` is a list of persons. Each person is represented as two lists (one for attribute names and the other for attribute values). The j-th element of attribute names corresponds to the j-th element of attribute values (linearized dictionary).
- `agents`: the two users engaged in the dialogue.
- `outcome_reward`: reward of the present dialogue.
- `events`: dictionary describing the dialogue. The j-th element of each sub-element of the dictionary describes the turn along the axis of the sub-element.
- `actions`: type of turn (either `message` or `select`).
- `agents`: who is talking? Agent 1 or 0?
- `data_messages`: the string exchanged if `action==message`. Otherwise, empty string.
- `data_selects`: selection of the user if `action==select`. Otherwise, empty selection/dictionary.
- `start_times`: always -1 in these data.
- `times`: sending time.
### Data Splits
There are 8967 dialogues for training, 1083 for validation and 1107 for testing.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{he-etal-2017-learning,
title = "Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings",
author = "He, He and
Balakrishnan, Anusha and
Eric, Mihail and
Liang, Percy",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P17-1162",
doi = "10.18653/v1/P17-1162",
pages = "1766--1776",
abstract = "We study a \textit{symmetric collaborative dialogue} setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.",
}
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
opus_finlex | null | 2022-11-03T16:08:11Z | 301 | 1 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fi",
"language:sv",
"license:unknown",
"region:us"
] | 2022-11-03T16:08:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fi
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusFinlex
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- fi
- sv
config_name: fi-sv
splits:
- name: train
num_bytes: 610550215
num_examples: 3114141
download_size: 153886554
dataset_size: 610550215
---
# Dataset Card for [opus_finlex]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[Finlex](http://opus.nlpl.eu/Finlex.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Swedish and Finnish.
### Languages
Swedish and Finnish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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telugu_news | null | 2023-01-25T14:45:35Z | 301 | 0 | null | [
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"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:te",
"license:unknown",
"region:us"
] | 2023-01-25T14:45:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- other
language:
- te
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
- text-classification
task_ids:
- language-modeling
- masked-language-modeling
- multi-class-classification
- topic-classification
pretty_name: TeluguNews
dataset_info:
features:
- name: sno
dtype: int32
- name: date
dtype: string
- name: heading
dtype: string
- name: body
dtype: string
- name: topic
dtype:
class_label:
names:
'0': business
'1': editorial
'2': entertainment
'3': nation
'4': sports
splits:
- name: train
num_bytes: 69400234
num_examples: 17312
- name: test
num_bytes: 17265514
num_examples: 4329
download_size: 0
dataset_size: 86665748
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news
- **Repository:** https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset
### Dataset Summary
This dataset contains Telugu language news articles along with respective topic
labels (business, editorial, entertainment, nation, sport) extracted from the daily Andhra Jyoti.
This dataset could be used to build Classification and Language Models.
### Supported Tasks and Leaderboards
Multiclass classification, Topic Classification, Language Model
### Languages
TE - Telugu, India
## Dataset Structure
### Data Instances
Two CSV files (train, test) with five columns (sno, date, heading, body, topic).
### Data Fields
- sno: id
- date: publish date of the news article
- heading: article heading/title
- body: article body/content
- topic: one of the following topics (business, editorial, entertainment, nation, sport)
### Data Splits
Train and Test
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
- https://www.kaggle.com/sudalairajkumar/telugu-nlp?select=telugu_news
- https://github.com/AnushaMotamarri/Telugu-Newspaper-Article-Dataset
#### Initial Data Collection and Normalization
The source data is scraped articles from archives of Telugu newspaper website Andhra Jyoti.
A set of queries were created and the corresponding ground truth answers were retrieved by a combination of BM25 and tf-idf.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Sudalai Rajkumar, Anusha Motamarri
### Licensing Information
[More Information Needed]
### Citation Information
```
@InProceedings{kaggle:dataset,
title = {Telugu News - Natural Language Processing for Indian Languages},
authors={Sudalai Rajkumar, Anusha Motamarri},
year={2019}
}
```
### Contributions
Thanks to [@oostopitre](https://github.com/oostopitre) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
turkish_shrinked_ner | null | 2023-01-25T14:54:44Z | 301 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other-turkish_ner",
"language:tr",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:54:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- expert-generated
language:
- tr
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- extended|other-turkish_ner
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: TurkishShrinkedNer
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-academic
'2': I-academic
'3': B-academic_person
'4': I-academic_person
'5': B-aircraft
'6': I-aircraft
'7': B-album_person
'8': I-album_person
'9': B-anatomy
'10': I-anatomy
'11': B-animal
'12': I-animal
'13': B-architect_person
'14': I-architect_person
'15': B-capital
'16': I-capital
'17': B-chemical
'18': I-chemical
'19': B-clothes
'20': I-clothes
'21': B-country
'22': I-country
'23': B-culture
'24': I-culture
'25': B-currency
'26': I-currency
'27': B-date
'28': I-date
'29': B-food
'30': I-food
'31': B-genre
'32': I-genre
'33': B-government
'34': I-government
'35': B-government_person
'36': I-government_person
'37': B-language
'38': I-language
'39': B-location
'40': I-location
'41': B-material
'42': I-material
'43': B-measure
'44': I-measure
'45': B-medical
'46': I-medical
'47': B-military
'48': I-military
'49': B-military_person
'50': I-military_person
'51': B-nation
'52': I-nation
'53': B-newspaper
'54': I-newspaper
'55': B-organization
'56': I-organization
'57': B-organization_person
'58': I-organization_person
'59': B-person
'60': I-person
'61': B-production_art_music
'62': I-production_art_music
'63': B-production_art_music_person
'64': I-production_art_music_person
'65': B-quantity
'66': I-quantity
'67': B-religion
'68': I-religion
'69': B-science
'70': I-science
'71': B-shape
'72': I-shape
'73': B-ship
'74': I-ship
'75': B-software
'76': I-software
'77': B-space
'78': I-space
'79': B-space_person
'80': I-space_person
'81': B-sport
'82': I-sport
'83': B-sport_name
'84': I-sport_name
'85': B-sport_person
'86': I-sport_person
'87': B-structure
'88': I-structure
'89': B-subject
'90': I-subject
'91': B-tech
'92': I-tech
'93': B-train
'94': I-train
'95': B-vehicle
'96': I-vehicle
splits:
- name: train
num_bytes: 200728389
num_examples: 614515
download_size: 0
dataset_size: 200728389
---
# Dataset Card for turkish_shrinked_ner
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://www.kaggle.com/behcetsenturk
### Dataset Summary
Shrinked processed version (48 entity type) of the turkish_ner.
Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
There's only the training set.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
Behcet Senturk
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@bhctsntrk](https://github.com/bhctsntrk) for adding this dataset. | [
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wikitext_tl39 | null | 2022-11-03T16:15:46Z | 301 | 0 | wikitext-tl-39 | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fil",
"language:tl",
"license:gpl-3.0",
"arxiv:1907.00409",
"region:us"
] | 2022-11-03T16:15:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fil
- tl
license:
- gpl-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: wikitext-tl-39
pretty_name: WikiText-TL-39
dataset_info:
features:
- name: text
dtype: string
config_name: wikitext-tl-39
splits:
- name: test
num_bytes: 46182996
num_examples: 376737
- name: train
num_bytes: 217182748
num_examples: 1766072
- name: validation
num_bytes: 46256674
num_examples: 381763
download_size: 116335234
dataset_size: 309622418
---
# Dataset Card for WikiText-TL-39
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Filipino Text Benchmarks](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:**
- **Paper:** [Evaluating language model finetuning techniques for low-resource languages](https://arxiv.org/abs/1907.00409)
- **Leaderboard:**
- **Point of Contact:** Jan Christian Blaise Cruz (jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
Large scale, unlabeled text dataset with 39 Million tokens in the training set. Inspired by the original WikiText Long Term Dependency dataset (Merity et al., 2016). TL means "Tagalog." Published in Cruz & Cheng (2019).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Filipino/Tagalog
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `text` (`str`)
The dataset is in plaintext and only has one field ("text") as it is compiled for language modeling.
### Data Splits
Split | Documents | Tokens
------|-----------|-------
Train | 120,975 | 39M
Valid | 25,919 | 8M
Test | 25,921 | 8M
Please see the paper for more details on the dataset splits
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Tagalog Wikipedia
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jcblaisecruz02](https://github.com/jcblaisecruz02) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Lacito/pangloss | Lacito | 2022-09-06T18:02:34Z | 301 | 3 | null | [
"task_categories:automatic-speech-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"source_datasets:original",
"language:jya",
"language:nru",
"license:cc-by-nc-sa-4.0",
"region:us"
] | 2022-09-06T18:02:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: Pangloss
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- jya
- nru
language_bcp47:
- x-japh1234
- x-yong1288
language_details: jya consists of japh1234 (Glottolog code); nru consists of yong1288 (Glottolog code)
license: cc-by-nc-sa-4.0
multilinguality:
- multilingual
- translation
size_categories:
yong1288:
- 10K<n<100K
japh1234:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids:
- speech-recognition
---
# Dataset Card for [Needs More Information]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Web interface of the Pangloss Collection, which hosts the data sets](https://pangloss.cnrs.fr/)
- **Repository:** [GithHub repository of the Pangloss Collection, which hosts the data sets](https://github.com/CNRS-LACITO/Pangloss/)
- **Paper:** [A paper about the Pangloss Collection, including a presentation of the Document Type Definition](https://halshs.archives-ouvertes.fr/halshs-01003734)
[A paper in French about the deposit in Zenodo](https://halshs.archives-ouvertes.fr/halshs-03475436)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Benjamin Galliot](mailto:b.g01lyon@gmail.com)
### Dataset Summary
Two audio corpora of minority languages of China (Japhug and Na), with transcriptions, proposed as reference data sets for experiments in Natural Language Processing. The data, collected and transcribed in the course of immersion fieldwork, amount to a total of about 1,900 minutes in Japhug and 200 minutes in Na. By making them available in an easily accessible and usable form, we hope to facilitate the development and deployment of state-of-the-art NLP tools for the full range of human languages. There is an associated tool for assembling datasets from the Pangloss Collection (an open archive) in a way that ensures full reproducibility of experiments conducted on these data.
The Document Type Definition for the XML files is available here:
http://cocoon.huma-num.fr/schemas/Archive.dtd
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Japhug (ISO 639-3 code: jya, Glottolog language code: japh1234) and Yongning Na (ISO 639-3 code: nru, Glottolog language code: yong1288) are two minority languages of China. The documents in the dataset have a transcription in the endangered language. Some of the documents have translations into French, English, and Chinese.
## Dataset Structure
### Data Instances
A typical data row includes the path, audio, sentence, document type and several translations (depending on the sub-corpus).
`
{
"path": "cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav",
"audio": "{'path': 'na/cocoon-db3cf0e1-30bb-3225-b012-019252bb4f4d_C1/Tone_BodyPartsOfAnimals_12_F4_2008_withEGG_069.wav', 'array': array([0.00018311, 0.00015259, 0.00021362, ..., 0.00030518, 0.00030518, 0.00054932], dtype=float32), 'sampling_rate': 16000}",
"sentence": "ʈʂʰɯ˧ | ɖɤ˧mi˧-ɬi˧pi˩ ɲi˩",
"doctype": "WORDLIST",
"translation:zh": "狐狸的耳朵",
"translation:fr": "oreilles de renard",
"translation:en": "fox's ears",
}
`
### Data Fields
path: the path to the audio file;;
audio: a dictionary containing the path to the audio file, the audio array and the sampling rate;
sentence: the sentence the native has pronunced;
doctype: the document type (a text or a word list);
translation:XX: the translation of the sentence in the language XX.
### Data Splits
The train, test and validation splits have all been reviewed and were splitted randomly (ratio 8:1:1) at sentence level (after the extraction from various files).
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset was collected in immersion fieldwork for language documentation. It contributes to the documentation and study of the world's languages by providing documents of connected, spontaneous speech recorded in their cultural context and transcribed in consultation with native speakers. The impacts concern research, and society at large: a guiding principle of the Pangloss Collection, which hosts the data sets, is that a close association between documentation and research is highly profitable to both. A range of possibilities for uses exist, for the scientific and speaker communities and for the general public.
### Discussion of Biases
The corpora are single-speaker and hence clearly do not reflect the sociolinguistic and dialectal diversity of the languages. No claim is made that the language variety described constitutes a 'standard'.
### Other Known Limitations
The translations are entirely hand-made by experts working on these languages; the amount and type of translations available varies from document to document, as not all documents have translations and not all translated documents have the same translation languages (Chinese, French, English...).
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Nexdata/accented_english | Nexdata | 2023-11-22T09:51:18Z | 301 | 5 | null | [
"task_categories:automatic-speech-recognition",
"language:en",
"region:us"
] | 2023-11-22T09:51:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the tagging app: https://github.com/huggingface/datasets-tagging
task_categories:
- automatic-speech-recognition
language:
- en
---
# Dataset Card for accented-english
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Homepage:** https://nexdata.ai/?source=Huggingface
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset contains 20,000 hours of accented English speech data. It's collected from local English speakers in more than 20 countries, such as USA, China, UK, Germany, Japan, India, France, Spain, Russia, Latin America, covering a variety of pronunciation habits and characteristics, accent severity, and the distribution of speakers. The format is 16kHz, 16bit, uncompressed wav, mono channel. The sentence accuracy is over 95%.
For more details, please refer to the link: https://nexdata.ai/speechRecognition?source=Huggingface
### Supported Tasks and Leaderboards
automatic-speech-recognition, audio-speaker-identification: The dataset can be used to train a model for Automatic Speech Recognition (ASR).
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Commercial License
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Emanuel/UD_Portuguese-Bosque | Emanuel | 2022-10-25T08:54:18Z | 301 | 1 | null | [
"language:pt",
"region:us"
] | 2022-10-25T08:54:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- pt
---
# AutoNLP Dataset for project: pos-tag-bosque
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Descritpion
This dataset has been automatically processed by AutoNLP for project pos-tag-bosque.
### Languages
The BCP-47 code for the dataset's language is pt.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"tags": [
5,
7,
0
],
"tokens": [
"Um",
"revivalismo",
"refrescante"
]
},
{
"tags": [
5,
11,
11,
11,
3,
5,
7,
1,
5,
7,
0,
12
],
"tokens": [
"O",
"7",
"e",
"Meio",
"\u00e9",
"um",
"ex-libris",
"de",
"a",
"noite",
"algarvia",
"."
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"tags": "Sequence(feature=ClassLabel(num_classes=17, names=['ADJ', 'ADP', 'ADV', 'AUX', 'CCONJ', 'DET', 'INTJ', 'NOUN', 'NUM', 'PART', 'PRON', 'PROPN', 'PUNCT', 'SCONJ', 'SYM', 'VERB', 'X'], names_file=None, id=None), length=-1, id=None)",
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 8328 |
| valid | 476 |
| [
-0.5035416483879089,
0.07136844098567963,
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0.21280382573604584,
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Fraser/mnist-text-no-spaces | Fraser | 2021-02-05T16:03:35Z | 301 | 1 | null | [
"region:us"
] | 2021-02-05T16:03:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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GEM/dstc10_track2_task2 | GEM | 2022-10-24T15:30:17Z | 301 | 4 | null | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"dialog-response-generation",
"region:us"
] | 2022-10-24T15:30:17Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: dstc10_track2_task2
tags:
- dialog-response-generation
---
# Dataset Card for GEM/dstc10_track2_task2
## Dataset Description
- **Homepage:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Repository:** https://github.com/alexa/alexa-with-dstc10-track2-dataset
- **Paper:** https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
- **Leaderboard:** https://eval.ai/challenge/1663/overview
- **Point of Contact:** Seokhwan Kim
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/dstc10_track2_task2).
### Dataset Summary
The DSTC10 Track2 Task 2 follows the DSTC9 Track1 task, where participants have to implement knowledge-grounded dialog systems.
The training dataset is inherited from the DSTC9 challenge and is in the written domain, while the test set is newly collected and consists of noisy ASR transcripts.
Hence, the dataset facilitates building models for grounded dialog response generation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/dstc10_track2_task2')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/dstc10_track2_task2).
#### website
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### paper
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### authors
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
https://github.com/alexa/alexa-with-dstc10-track2-dataset
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
https://assets.amazon.science/54/a1/5282d47044179737b4289622c824/how-robust-are-you-evaluating-task-oriented-dialogue-systems-on-spoken-conversations.pdf
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
@inproceedings{kim2021robust,
title={" How Robust ru?": Evaluating Task-Oriented Dialogue Systems on Spoken Conversations},
author={Kim, Seokhwan and Liu, Yang and Jin, Di and Papangelis, Alexandros and Gopalakrishnan, Karthik and Hedayatnia, Behnam and Hakkani-Tur, Dilek},
journal={IEEE Automatic Speech Recognition and Understanding Workshop},
year={2021}
}
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Seokhwan Kim
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
seokhwk@amazon.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
yes
#### Leaderboard Link
<!-- info: Provide a link to the leaderboard. -->
<!-- scope: periscope -->
https://eval.ai/challenge/1663/overview
#### Leaderboard Details
<!-- info: Briefly describe how the leaderboard evaluates models. -->
<!-- scope: microscope -->
It evaluates the models based on the automatic metrics defined in the task paper for the three tasks of detection, selection and generation.
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`En`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To conduct research on dialogue state tracking and knowledge-grounded response generation.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
This dataset aims to explore the robustness of conversational models when trained on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Amazon
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Seokhwan Kim, Yang Liu, Di Jin, Alexandros Papangelis, Karthik Gopalakrishnan, Behnam Hedayatnia, Dilek Hakkani-Tur (Amazon Alexa AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Amazon
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Alexandros Papangelis (Amazon Alexa AI), Di Jin (Amazon Alexa AI), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
features = datasets.Features(
{
"id": datasets.Value("string"),
"gem_id": datasets.Value("string"),
"turns": [
{
"speaker": datasets.Value("string"),
"text": datasets.Value("string"),
"nbest": [
{
"hyp": datasets.Value("string"),
"score": datasets.Value("float"),
}
],
}
],
"knowledge": {
"domain": datasets.Value("string"),
"entity_name": datasets.Value("string"),
"title": datasets.Value("string"),
"body": datasets.Value("string"),
},
"response": datasets.Value("string"),
"source": datasets.Value("string"),
"linearized_input": datasets.Value("string"),
"target": datasets.Value("string"),
"references": [datasets.Value("string")],
}
)
nbest contains an nbest list of outputs generated by an ASR system along with their scores.
knowledge defines the annotated grounding as well as its metadata
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
It was kept compatible with MultiWox 2.X data.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
{'id': '0',
'gem_id': 'GEM-dstc10_track2_task2-test-0',
'turns': [{'speaker': 'U',
'text': "hi uh i'm looking for restaurant in lower ha",
'nbest': [{'hyp': "hi uh i'm looking for restaurant in lower ha",
'score': -25.625450134277344},
{'hyp': "hi uh i'm looking for restaurant in lower hai",
'score': -25.969446182250977},
{'hyp': "hi uh i'm looking for restaurant in lower haig",
'score': -32.816890716552734},
{'hyp': "hi uh i'm looking for restaurant in lower haigh",
'score': -32.84316635131836},
{'hyp': "hi uh i'm looking for restaurant in lower hag",
'score': -32.8637580871582},
{'hyp': "hi uh i'm looking for restaurant in lower hah",
'score': -33.1048698425293},
{'hyp': "hi uh i'm looking for restaurant in lower hait",
'score': -33.96509552001953},
{'hyp': "hi um i'm looking for restaurant in lower hai",
'score': -33.97885513305664},
{'hyp': "hi um i'm looking for restaurant in lower haig",
'score': -34.56083679199219},
{'hyp': "hi um i'm looking for restaurant in lower haigh",
'score': -34.58711242675781}]},
{'speaker': 'S',
'text': 'yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for',
'nbest': []},
{'speaker': 'U',
'text': 'yeah umm am looking for an expensive restaurant',
'nbest': [{'hyp': 'yeah umm am looking for an expensive restaurant',
'score': -21.272899627685547},
{'hyp': 'yeah umm m looking for an expensive restaurant',
'score': -21.444047927856445},
{'hyp': 'yeah umm a m looking for an expensive restaurant',
'score': -21.565458297729492},
{'hyp': 'yeah ummm am looking for an expensive restaurant',
'score': -21.68832778930664},
{'hyp': 'yeah ummm m looking for an expensive restaurant',
'score': -21.85947608947754},
{'hyp': 'yeah ummm a m looking for an expensive restaurant',
'score': -21.980886459350586},
{'hyp': "yeah umm a'm looking for an expensive restaurant",
'score': -22.613924026489258},
{'hyp': "yeah ummm a'm looking for an expensive restaurant",
'score': -23.02935218811035},
{'hyp': 'yeah um am looking for an expensive restaurant',
'score': -23.11180305480957},
{'hyp': 'yeah um m looking for an expensive restaurant',
'score': -23.28295135498047}]},
{'speaker': 'S',
'text': "lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive",
'nbest': []},
{'speaker': 'U',
'text': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'nbest': [{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.492507934570312},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price range",
'score': -27.75853729248047},
{'hyp': "sure ummm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.44410514831543},
{'hyp': "sure umm maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -29.710134506225586},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price range",
'score': -31.136560440063477},
{'hyp': "sure um maybe if you don't have anything expensive how about something in the moderate price rang",
'score': -33.088157653808594},
{'hyp': "sure ummm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.127620697021484},
{'hyp': "sure umm maybe i you don't have anything expensive how about something in the moderate price range",
'score': -36.39365005493164},
{'hyp': "sure ummm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.43605041503906},
{'hyp': "sure umm maybe if yo don't have anything expensive how about something in the moderate price range",
'score': -36.70207977294922}]},
{'speaker': 'S',
'text': 'ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound',
'nbest': []},
{'speaker': 'U',
'text': 'that sounds good ummm do they have any sort of happy hour special',
'nbest': [{'hyp': 'that sounds good ummm do they have any sort of happy hour special',
'score': -30.316478729248047},
{'hyp': 'that sounds good umm do they have any sort of happy hour special',
'score': -30.958009719848633},
{'hyp': 'that sounds good um do they have any sort of happy hour special',
'score': -34.463165283203125},
{'hyp': 'that sounds good ummm do they have any sirt of happy hour special',
'score': -34.48350143432617},
{'hyp': 'that sounds good umm do they have any sirt of happy hour special',
'score': -35.12503433227539},
{'hyp': 'that sounds good ummm do they have any sord of happy hour special',
'score': -35.61939239501953},
{'hyp': 'that sounds good umm do they have any sord of happy hour special',
'score': -36.26092529296875},
{'hyp': 'that sounds good ummm do they have any sont of happy hour special',
'score': -37.697105407714844},
{'hyp': 'that sounds good umm do they have any sont of happy hour special',
'score': -38.33863830566406},
{'hyp': 'that sounds good um do they have any sirt of happy hour special',
'score': -38.630191802978516}]}],
'knowledge': {'domain': 'restaurant',
'entity_name': 'The View Lounge',
'title': 'Does The View Lounge offer happy hour?',
'body': 'The View Lounge offers happy hour.'},
'response': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'source': 'sf_spoken',
'linearized_input': "<U> hi uh i'm looking for restaurant in lower ha <S> yeah definitely i can go ahead and help you with that ummm what kind of option in a restaurant are you looking for <U> yeah umm am looking for an expensive restaurant <S> lemme go ahead and see what i can find for you ok great so i do ummm actually no i'm sorry is there something else i can help you find i don't see anything expensive <U> sure ummm maybe if you don't have anything expensive how about something in the moderate price range <S> ok moderate lemme go ahead and check to see what i can find for moderate ok great i do have several options coming up how does the view lounge sound <U> that sounds good ummm do they have any sort of happy hour special || knowledge domain: restaurant, entity: The View Lounge, title: Does The View Lounge offer happy hour?, information: The View Lounge offers happy hour.",
'target': 'uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour',
'references': ['uhhh great question lemme go ahead and check that out for you ok fantastic so it looks like they do offer happy hour']}
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
train: training set, val: validation set, test: test set
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The track dataset originally only consists of a validation and test set in the spoken domain with noisy ASR transcripts.
The training set is taken from the predecessor task DSTC9 Track 1 and contains written conversations.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to understand language by tracking the dialogue state, and their ability to generate knowledge-grounded responses.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
This dataset contains transcribed spoken interactions.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
We can measure the model’s ability to understand language and to generate knowledge-grounded responses.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
This dataset can be used to evaluate conversational models on spoken inputs (using ASR hypotheses). In particular, we can evaluate the models’ ability to generate knowledge-grounded responses.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
BLEU-1, BLEU-2, BLEU-3, BLEU-4, METEOR, ROGUE-1, ROGUE-2, ROGUE-L
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
We want to explore how conversational models perform on spoken data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
This dataset aims to explore the robustness of conversational models when evaluated on spoken data. It has two aspects, multi-domain dialogue state tracking and conversation modeling with access to unstructured knowledge.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The conversations revolve around 5 domains (or topics): hotels, restaurants, attractions, taxi, train.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The subjects were instructed to conduct fictional conversations about booking restaurants or requesting fictional information.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
There should be no risk related to PII as the subjects conduct fictional conversations.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
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GEM/surface_realisation_st_2020 | GEM | 2022-10-24T15:30:30Z | 301 | 0 | null | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:ar",
"language:zh",
"language:en",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:pt",
"language:ru",
"language:es",
"license:cc-by-2.5",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- ar
- zh
- en
- fr
- hi
- id
- ja
- ko
- pt
- ru
- es
license:
- cc-by-2.5
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: surface_realisation_st_2020
tags:
- data-to-text
---
# Dataset Card for GEM/surface_realisation_st_2020
## Dataset Description
- **Homepage:** http://taln.upf.edu/pages/msr2020-ws/SRST.html#data
- **Repository:** https://sites.google.com/site/genchalrepository/surface-realisation/sr-20-multilingual
- **Paper:** https://aclanthology.org/2020.msr-1.1/
- **Leaderboard:** N/A
- **Point of Contact:** Simon Mille
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/surface_realisation_st_2020).
### Dataset Summary
This dataset was used as part of the multilingual surface realization shared task in which a model gets full or partial universal dependency structures and has to reconstruct the natural language. This dataset support 11 languages.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/surface_realisation_st_2020')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/surface_realisation_st_2020).
#### website
[Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html#data)
#### paper
[ACL Anthology](https://aclanthology.org/2020.msr-1.1/)
#### authors
Simon Mille (Pompeu Fabra University); Leo Wanner (Pompeu Fabra University); Anya Belz (Brighton University); Bernd Bohnet (Google Inc.); Thiago Castro Ferreira (Federal University of Minas Gerais); Yvette Graham (ADAPT/Trinity College Dublin)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html#data)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Website](https://sites.google.com/site/genchalrepository/surface-realisation/sr-20-multilingual)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.msr-1.1/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{mille-etal-2020-third,
title = "The Third Multilingual Surface Realisation Shared Task ({SR}{'}20): Overview and Evaluation Results",
author = "Mille, Simon and
Belz, Anya and
Bohnet, Bernd and
Castro Ferreira, Thiago and
Graham, Yvette and
Wanner, Leo",
booktitle = "Proceedings of the Third Workshop on Multilingual Surface Realisation",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.msr-1.1",
pages = "1--20",
abstract = "This paper presents results from the Third Shared Task on Multilingual Surface Realisation (SR{'}20) which was organised as part of the COLING{'}20 Workshop on Multilingual Surface Realisation. As in SR{'}18 and SR{'}19, the shared task comprised two tracks: (1) a Shallow Track where the inputs were full UD structures with word order information removed and tokens lemmatised; and (2) a Deep Track where additionally, functional words and morphological information were removed. Moreover, each track had two subtracks: (a) restricted-resource, where only the data provided or approved as part of a track could be used for training models, and (b) open-resource, where any data could be used. The Shallow Track was offered in 11 languages, whereas the Deep Track in 3 ones. Systems were evaluated using both automatic metrics and direct assessment by human evaluators in terms of Readability and Meaning Similarity to reference outputs. We present the evaluation results, along with descriptions of the SR{'}19 tracks, data and evaluation methods, as well as brief summaries of the participating systems. For full descriptions of the participating systems, please see the separate system reports elsewhere in this volume.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Simon Mille
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
sfmille@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
No multiple dialects.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Arabic`, `Chinese`, `English`, `French`, `Hindi`, `Indonesian`, `Japanese`, `Korean`, `Portuguese`, `Russian`, `Spanish, Castilian`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Unknown
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-2.5: Creative Commons Attribution 2.5 Generic
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset is intended to be used for training models to solve several NLG subtasks, such as function word introduction, morphological agreement resolution, word order determination and inflection generation.
Comment about the license: the dataset has multiple licences, since each original dataset has their own type of licence. All datasets but one are CC-BY and subclasses of it, the other one is GPL (French Sequoia).
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
The models are able to introduce surface features (syntax, morphology, topology) from more or less abstract inputs in different, the most abstract being predicate-argument structures. The datasets cover a large variety of domains (news, blogs, forums, wikipedia pages, etc.).
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`, `academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Pompeu Fabra University, Google Inc., University of Brighton, Federal University of Minas Gerais, ADAPT/Trinity College Dublin
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Simon Mille (Pompeu Fabra University); Leo Wanner (Pompeu Fabra University); Anya Belz (Brighton University); Bernd Bohnet (Google Inc.); Thiago Castro Ferreira (Federal University of Minas Gerais); Yvette Graham (ADAPT/Trinity College Dublin)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Mostly EU funds via H2020 projects
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Simon Mille (Pompeu Fabra University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
`input` (string): this field contains an input tree in CoNLL-U format; the CoNLL-U format is a one-word-per-line format with the following tab-separated 10 columns (see [here](http://universaldependencies.org/format.html)): [1] Position, [2] Lemma, [3] Wordform, [4] Part of Speech, [5] Fine-grained Part of Speech (if available), [6] Features (FEATS), [7] governor, [8] dependency relation, [9] additional dependency information, and [10] metadata. For the surface task, the input is a Universal Dependency tree of a given language in which the word order was scrambled and the surface forms removed (only lemmas are available); for the deep task, the input is a tree derived from the surface input, with predicate-argument relations between content words only (function words were removed) and without any morphological agreement information.
`target_tokenized` (string): this field contains the target sentence to generate, in which every non-initial and non-final token is surrounded by two spaces. This output is usually used for automatic evaluations.
`target` (string): this field contains the detokenised target sentence to generate. This output is usually used for human evaluations.
`gem_id` (string): a unique ID.
`sentence_id` (string): the original ID of a sentence in the UD dataset.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The structure of the input (CoNLL-U) was chosen according to the standards in parsing, and because the original UD datasets were provided in this format.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The input labels for the surface track are the original labels in the UD treebanks; see [here](https://universaldependencies.org/u/dep/index.html) for the dependencies, [here](https://universaldependencies.org/u/feat/index.html) for the features, and [here](https://universaldependencies.org/u/pos/index.html) for the PoS tags.
The input labels for the deep track are a subset of the PoS tags and features of the surface track, and for the relations, universal predicate-argument relations augmented with a few specific relations to capture coordinations and named entity relations for instance.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{"input": "1\tGoogle\t_\tPROPN\tNNP\tNumber=Sing\t5\tnsubj\t_\t_\n2\t\t_\tPUNCT\t.\tlin=+1\t5\tpunct\t_\t_\n3\tinto\t_\tADP\tIN\t_\t6\tcase\t_\t_\n4\tif\t_\tSCONJ\tIN\t_\t5\tmark\t_\t_\n5\tmorph\t_\tVERB\tVBD\tMood=Ind|Tense=Past|VerbForm=Fin\t7\tadvcl\t_\t_\n6\tGoogleOS\t_\tPROPN\tNNP\tNumber=Sing\t5\tobl\t_\t_\n7\twhat\t_\tPRON\tWP\tPronType=Int\t0\troot\t_\t_", "target_tokenized": "What if Google Morphed Into GoogleOS ?", "target": "What if Google Morphed Into GoogleOS?", "gem_id": "GEM-surface_realisation_st_2020-T1-test-en_ewt-ud-test-0", "sentence_id": ""}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
There are 119 splits in the dataset:
- 29 training sets, which correspond to 20 UD datasets (11 languages), 9 of which have both surface and deep inputs (3 languages);
- 29 development set which correspond to the 29 training sets above;
- 29 test sets for the data described above;
- 4 out-of-domain test sets, 3 surface inputs and 1 deep one (3 languages for which PUD out-of-domain datasets were available);
- 9 automatically parsed in-domain test sets, 6 surface inputs and 3 deep inputs (6 languages for which good UD parsers were available);
- 9 automatically parsed out-of-domain test sets, 6 surface inputs and 3 deep inputs (6 languages for which we were able to create clean Wikipedia text and that had a good UD parser).
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
Described above for more clarity.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
An outlier would usually be an input that corresponds to a very long sentence (e.g. 159 words in English, when the average number of words per sentence is around 25).
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The datset includes languages from different families and some languages not often used in NLG (e.g. Arabic, Indonesian, Korean, Hindi). It proposes two tasks, which can be tackled both separately and in one shot, with different levels of difficulty: the most superficial task (T1) consits in ordering and inflecting some trees, and the deeper task (T2) includes extra tasks such as defining the syntactic structure and introducing function words and morphological agreement information. Both tasks can allow for developing modules for pipeline NLG architectures. T1 is rather straightforward to evaluate: BLEU works quite well for some languages since all the words are present in the input and few word orders only can be possible for a syntactic tree. But T2 is more challenging to evaluate, since more outputs are correct given one particular input.
There is a large variety of sizes in the datasets, both clean and noisy data, parallel data in different languages, and many already available system outputs to use as baselines.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
This is possibly the only dataset that starts the generation process from predicate-argument structures and from syntactic structures. It also has parallel datasets in a few languages (coming from the PUD parallel annotations).
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Syntacticisation, functional word introduction, word order resolution, agreement resolution, morphological inflection
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Website](http://taln.upf.edu/pages/msr2020-ws/SRST.html)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
Syntacticisation: prediction of the syntactic
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Syntacticisation, functional word introduction, word order resolution, morphological agreement resolution, morphological inflection
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `BERT-Score`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
NIST: n-gram similarity metric weighted in favour of less frequent n-grams which are taken to be more informative.
Normalised edit distance (DIST): inverse, normalised, character-based string-edit distance that starts by computing the minimum number of character inserts, deletes and substitutions (all at cost 1) required to turn the system output into the (single) reference text.
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU, NIST, BERTScore and DIST simply aim at calculating in different ways the similarity between a predicted and a reference sentence.
Two additional criteria have been used for human evaluation, Readability and Meaning SImilarity. The statement to be assessed in the Readability evaluation was: "The text reads well and is free from grammatical errors and awkward constructions.". The corresponding statement in the Meaning Similarity evaluation, in which system outputs (‘the black text’) were compared to reference sentences (‘the gray text’), was: "The meaning of the gray text is adequately expressed by the black text."
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Same as above.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
- [Fast and Accurate Non-Projective Dependency Tree Linearization](https://aclanthology.org/2020.acl-main.134/)
- [Shape of Synth to Come: Why We Should Use Synthetic Data for English Surface Realization](https://aclanthology.org/2020.acl-main.665/)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The datasets were created in the context of the Surface Realisation Shared Task series.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The dataset's objective was to allow for training systems to perform tasks related to surface realisation (introduction of function words, syntacticisation, resolution of morphological agreements, word order resolution, inflection generation.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
Each of the 20 used UD datasets comes from various sources, all listed on the individual page of each UD treeebank (https://universaldependencies.org/).
Additional test sets were created for the task, and were obtained from Wikipedia pages for 6 languages.
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Multiple websites`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
There are numerous sources of language in the multiple datasets.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
There is a large variety of topics in the multiple datasets.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
The text data was detokenised so as to create references for automatic evaluations (several languages don't use spaces to separate words, and running metrics like BLEU would not make sense without separating all the tokens in a sentence).
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
For the Wikipedia test created for the shared task, extensive filtering was applied to achieve reasonably good text quality. Sentences that include special characters, contain unusual tokens (e.g. ISBN), or have unbalanced quotation marks or brackets were skipped. Furthermore, only sentences with more than 5 tokens and shorter than 50 tokens were selected. After the initial filtering, quite a few malformed sentences remained. In order to remove those, the sentences were scored with BERT and
only the top half scored sentences were kept. Finally, via manual inspection, patterns and expressions were identified to
further reduce the number of malformed sentences.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The Universal Dependency data had been previously used for shared tasks on parsing, so it made sense to reuse it for generation.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
Thanks to the original work of the UD dataset creators, the surface realisation dataset addresses a few languages which are possibly under-served in NLG: e.g. Arabic, Hindi, Indonesian, Korean.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
It is very likely that the distribution of language producers is not fully represented in the datasets of each language.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
No risks foreseen.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`multiple licenses`, `open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`multiple licenses`, `open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The deep track inputs (predicate-argument structures) are not of perfect quality, they were derived automatically from gold or predicted syntactic parses using handcrafted grammars.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
The datasets are probably not fitted to train tools to produce "unusual" languages (e.g. poetry, kid writing etc.).
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
To be thought of :)
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GEM-submissions/GEM__bart_base_schema_guided_dialog__1645547915 | GEM-submissions | 2022-02-22T16:38:38Z | 301 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-22T16:38:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: BART_BASE_schema_guided_dialog
---
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HenryAI/KerasAPIReference.txt | HenryAI | 2021-12-15T15:55:07Z | 301 | 0 | null | [
"region:us"
] | 2021-12-15T15:55:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Keras API from https://keras.io/api/ <br />
Formatted into .txt file for input to https://huggingface.co/blog/how-to-train | [
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HenryAI/KerasBERTv1-Data | HenryAI | 2021-12-15T16:05:48Z | 301 | 0 | null | [
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HenryAI/KerasCodeExamples.txt | HenryAI | 2021-12-15T15:57:06Z | 301 | 0 | null | [
"region:us"
] | 2021-12-15T15:57:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Keras Code Examples from https://keras.io/examples/ <br />
organized as .txt file for input to this HF tutorial: <br />
https://huggingface.co/blog/how-to-train | [
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Ishwar/Senti | Ishwar | 2021-10-31T10:03:41Z | 301 | 0 | null | [
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Lucylulu/imdb | Lucylulu | 2021-12-08T20:20:37Z | 301 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Motahar/github-issues | Motahar | 2022-10-25T09:06:29Z | 301 | 0 | null | [
"task_categories:text-retrieval",
"task_categories:text-classification",
"task_ids:document-retrieval",
"task_ids:multi-label-classification",
"task_ids:multi-class-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-10-25T09:06:29Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: Huggingface Datasets github issues
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-retrieval
- text-classification
task_ids:
- document-retrieval
- multi-label-classification
- multi-class-classification
--- | [
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Narsil/test_data | Narsil | 2022-02-14T11:24:59Z | 301 | 0 | null | [
"region:us"
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NbAiLab/NPSC_test | NbAiLab | 2022-11-07T12:37:31Z | 301 | 0 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:audio-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:2G<n<1B",
"source_datasets:original",
"language:nb",
"language:no",
"language:nn",
"license:cc0-1.0",
"speech-modeling",
"region:us"
] | 2022-11-07T12:37:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- nb
- 'no'
- nn
license:
- cc0-1.0
multilinguality:
- monolingual
size_categories:
- 2G<n<1B
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- audio-classification
task_ids:
- speech-modeling
pretty_name: NPSC
tags:
- speech-modeling
---
# Dataset Card for NBAiLab/NPSC
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Data Fields](#data-fiels)
- [Dataset Creation](#dataset-creation)
- [Statistics](#statistics)
- [Document Types](#document-types)
- [Languages](#languages)
- [Publish Periode](#publish-periode)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://www.nb.no/sprakbanken/
- **Repository:** https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/
- **Paper:** https://www.nb.no/sprakbanken/
- **Point of Contact:** [Per Erik Solberg](mailto:per.solberg@nb.no)
The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models. The corpus is created by Språkbanken at the National Library in Norway.
NPSC is based on sound recording from meeting in the Norwegian Parliament. These talks are orthographically transcribed to either Norwegian Bokmål or Norwegian Nynorsk. In addition to the data actually included in this dataset, there is a significant amount of metadata that is included in the original corpus. Through the speaker id there is additional information about the speaker, like gender, age, and place of birth (ie dialect). Through the proceedings id the corpus can be linked to the official proceedings from the meetings.
The corpus is in total sound recordings from 40 entire days of meetings. This amounts to 140 hours of speech, 65,000 sentences or 1.2 million words.
This corpus is an adaption of the original corpus made for efficiant ASR training. For simplicity and portability, a few of the original datasets features, like the token transcription, is ommitted. You can find the complete dataset at [the Resource Catalogue at Språkbanken](https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-58/).
## How to Use (This needs to be edited of course)
```python
from datasets import load_dataset
data = load_dataset("nb/NPSC", streaming=True)
```
## Data Fields
Currently there are two versions included in this repo.
### Version A
This verison has a short list of the metadata and includes the audio (48k mp3) encoded as a float32 array in the dataset itself.
The current dataloader script is associated with this version.
One line in train.json looks like this:
```json
{
"sentence_id": 7309,
"sentence_order": 0,
"speaker_id": 1,
"speaker_name": "Marit Nybakk",
"sentence_text": "Stortingets møte er lovlig satt",
"sentence_language_code": "nb-NO",
"text": "Stortingets møte er lovlig satt",
"start_time": 302650,
"end_time": 306000,
"normsentence_text": "Stortingets møte er lovlig satt",
"transsentence_text": "Stortingets møte er lovleg sett",
"translated": 1,
"audio": {
"path": "audio/20170207-095506_302650_306000.wav",
"array": [
24,
25,
50,
(...)
],
"sampling_rate": 48000
}
}
```
### Version B
This verison does not contain the audio encoded in the dataset. Instead it has the audio files placed in sub-directories. There are currently both samples in clips_48k_wav and clips_16k_mp3. Only the base filename is referred in the dataset. Please not that there are both sentence-based audio clips as well at meeting-based audio clips. The dataset contains referrals to both, the latter referral has start and stop time as well.
One line in the train/metadata.json looks like this:
```json
{
"meeting_date": "20170207",
"full_audio_file": "20170207-095506",
"proceedings_file": "20170207-095506.ref",
"duration": 4442474,
"transcriber_id": 1,
"reviewer_id": 2,
"data_split": "test",
"speaker_name": "Marit Nybakk",
"speaker_id": 1,
"sentence_id": 7309,
"sentence_language_code": "nb-NO",
"sentence_text": "Stortingets møte er lovlig satt",
"sentence_order": 0,
"audio_file": "20170207-095506_302650_306000",
"start_time": 302650,
"end_time": 306000,
"normsentence_text": "Stortingets møte er lovlig satt",
"transsentence_text": "Stortingets møte er lovleg sett",
"translated": 1
}
```
### Dataset Creation
We are providing a **train**, **dev** and **test** split. These are the same as in the orginal corpus.
Build date: 20012022
#### Initial Data Collection and Curation
The procedure for the dataset creation is described in detail in the paper.
## Statistics
| Feature | Value |
|:---------|-----------:|
| Duration, pauses included | 140,3 hours|
| Duration, pauses not included | 125,7 hours |
| Word count | 1,2 million |
| Sentence count | 64.531 |
| Language distribution | Nynorsk: 12,8%|
| | Bokmål: 87,2%%|
| Gender distribution | Female: 38,3% |
| | Male: 61.7% |
## Considerations for Using the Data
This corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.
### Discussion of Biases
Please refer to our paper.
### Dataset Curators
[Per Erik Solberg](mailto:per.solberg@nb.no)
[Freddy Wetjen](mailto:Freddy.wetjen@nb.no), [Andre Kaasen](mailto:andre.kasen@nb.no) and [Per Egil Kummervold](mailto:per.kummervold@nb.no) has contributed to porting it to the Hugging Face Dataset format.
### Licensing Information
Licensed for use outside the National Library of Norway.
## License
CC-ZERO(https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
We are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:
```
ANDRE: TO BE DONE
```
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PaulLerner/triviaqa_splits_for_viquae | PaulLerner | 2022-02-22T13:58:30Z | 301 | 0 | null | [
"region:us"
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Plim/common_voice_7_0_fr_processed | Plim | 2022-12-12T09:12:53Z | 301 | 0 | null | [
"language:fr",
"region:us"
] | 2022-12-12T09:12:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
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RuudVelo/nl_corpora_parliament_processed | RuudVelo | 2022-01-27T13:47:05Z | 301 | 0 | null | [
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Sakonii/nepalitext-language-model-dataset | Sakonii | 2022-10-25T06:14:22Z | 301 | 3 | null | [
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:extended|oscar",
"source_datasets:extended|cc100",
"language:ne",
"license:cc0-1.0",
"region:us"
] | 2022-10-25T06:14:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
- other
language:
- ne
license:
- cc0-1.0
multilinguality:
- monolingual
source_datasets:
- extended|oscar
- extended|cc100
task_categories:
- text-generation
task_ids:
- language-modeling
pretty_name: nepalitext-language-model-dataset
---
# Dataset Card for "nepalitext-language-model-dataset"
### Dataset Summary
"NepaliText" language modeling dataset is a collection of over 13 million Nepali text sequences (phrases/sentences/paragraphs) extracted by combining the datasets: [OSCAR](https://huggingface.co/datasets/oscar) , [cc100](https://huggingface.co/datasets/cc100) and a set of scraped Nepali articles on Wikipedia.
### Supported Tasks and Leaderboards
This dataset is intended to pre-train language models and word representations on Nepali Language.
### Languages
The data is focused on Nepali language, but may have instances of other languages as well.
## Dataset Structure
### Data Instances
An example:
```
{'text': 'घरेलु मैदानमा भएको च्याम्पियन्स लिगको दोस्रो लेगमा एथ्लेटिको मड्रिडले आर्सनललाई एक शून्यले हराउँदै समग्रमा दुई एकको अग्रताका साथ फाइनलमा प्रवेश गरेको हो ।\n'}
```
### Data Fields
The data fields are:
- `text`: a `string` feature.
### Data Splits
train|test|
----:|---:|
13141222|268189|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
Being extracted and scraped from variety of internet sources, Personal and sensitive information might be present. This must be considered before training deep learning models, specially in the case of text-generation models.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@Sakonii](https://github.com/Sakonii) for adding this dataset. | [
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SaulLu/Natural_Questions_HTML | SaulLu | 2021-08-27T18:58:58Z | 301 | 0 | null | [
"region:us"
] | 2021-08-27T18:58:58Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is a dataset extracted from the Natural Questions dataset
This dataset is currently under development | [
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SaulLu/Natural_Questions_HTML_Toy | SaulLu | 2021-09-17T14:54:03Z | 301 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SetFit/TREC-QC | SetFit | 2022-01-15T22:42:56Z | 301 | 0 | null | [
"region:us"
] | 2022-01-15T22:42:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # TREC Question Classification
Question classification in coarse and fine-grained categories.
Source:
[Experimental Data for Question Classification](https://cogcomp.seas.upenn.edu/Data/QA/QC/)
Xin Li, Dan Roth, Learning Question Classifiers. COLING'02, Aug., 2002. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/the-2022-trucker-strike-on-reddit | SocialGrep | 2022-07-01T18:00:49Z | 301 | 1 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T18:00:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-2022-trucker-strike-on-reddit
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets/the-2022-trucker-strike-on-reddit?utm_source=huggingface&utm_medium=link&utm_campaign=the2022truckerstrikeonreddit)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=the2022truckerstrikeonreddit)
### Dataset Summary
This corpus contains all the comments under the /r/Ottawa convoy megathreads.
Comments are annotated with their score.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a Reddit comment.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'sentiment': the evaluated sentiment of the data point, if any.
- 'body': the text of the data point.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC-BY v4.0
### Contributions
[Needs More Information] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SuperAI2-Machima/Yord_ThaiQA_LST20 | SuperAI2-Machima | 2022-02-25T06:31:36Z | 301 | 0 | null | [
"region:us"
] | 2022-02-25T06:31:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | พี่ยอด และน้อง ๆ ในทีมบ้านมัณิชมา ร่วมกันสร้างชุดข้อมูล คำถาม - คำตอบ จากชุดข้อมูล LST-20
โดยใช้ POS และ NER เพื่อมาสร้างชุดประโยคคำถาม
ได้ข้อมูลคำถาม - ตอบ ทั้งหมดประมาณ 1,000 แถว
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TurkuNLP/turku_hockey_data2text | TurkuNLP | 2021-11-30T13:41:47Z | 301 | 0 | null | [
"region:us"
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abidlabs/crowdsourced-speech7 | abidlabs | 2022-01-21T17:21:47Z | 301 | 0 | null | [
"region:us"
] | 2022-01-21T17:21:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
arjundd/meddlr-data | arjundd | 2022-03-23T23:30:18Z | 301 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2022-03-23T23:30:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
license: apache-2.0
---
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astrideducation/cefr-combined-no-cefr-test | astrideducation | 2021-12-02T14:47:44Z | 301 | 1 | null | [
"region:us"
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cestwc/adapted-msrcomp | cestwc | 2021-12-16T06:34:25Z | 301 | 0 | null | [
"region:us"
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chenghao/scielo_books | chenghao | 2022-07-01T18:34:59Z | 301 | 0 | null | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:pt",
"language:es",
"license:cc-by-nc-sa-3.0",
"region:us"
] | 2022-07-01T18:34:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
- pt
- es
license:
- cc-by-nc-sa-3.0
multilinguality:
- multilingual
paperswithcode_id: null
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
## Dataset Description
- **Homepage:** [scielo.org](https://search.livros.scielo.org/search/?fb=&where=BOOK&filter%5Bis_comercial_filter%5D%5B%5D=f)
### Dataset Summary
This dataset contains all text from open-access PDFs on [scielo.org](https://search.livros.scielo.org/search/?fb=&where=BOOK&filter%5Bis_comercial_filter%5D%5B%5D=f). As of Dec. 5 2021, the total number of books available is 962. Some of them are not in native PDF format (e.g. scanned images) though.
### Supported Tasks and Leaderboards
- `sequence-modeling` or `language-modeling`: The dataset can be used to train a language model.
### Languages
As of Dec. 5 2021, there are 902 books in Portuguese, 55 in Spanish, and 5 in English.
## Dataset Structure
### Data Instances
Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
```
{
"sbid":"23pcw",
"id":"23pcw",
"shortname":"",
"title":"Educa\u00e7\u00e3o, sa\u00fade e esporte: novos\tdesafios \u00e0 Educa\u00e7\u00e3o F\u00edsica",
"eisbn":"9788574554907",
"isbn":"9788574554273",
"author":"Farias, Gelcemar Oliveira; Nascimento, Juarez Vieira do",
"corporate_authors":"",
"translators":"",
"coordinators":"",
"editors":"",
"others":"",
"organizers":"",
"collaborators":"",
"publisher":"Editus",
"language":"pt",
"year": 2016,
"synopsis":"\"A colet\u00e2nea contempla cap\u00edtulos que discutem a Educa\u00e7\u00e3o F\u00edsica a partir dos pressupostos da Educa\u00e7\u00e3o, da Sa\u00fade e do Esporte, enquanto importante desafio do momento atual e diante dos avan\u00e7os e das mudan\u00e7as que se consolidaram na forma\u00e7\u00e3o inicial em Educa\u00e7\u00e3o F\u00edsica. A obra convida a todos para a realiza\u00e7\u00e3o de futuras investiga\u00e7\u00f5es, no sentido de concentrar esfor\u00e7os para o fortalecimento de n\u00facleos de estudos e a sistematiza\u00e7\u00e3o de linhas de pesquisa.\"",
"format":"",
"type":"book",
"is_public":"true",
"is_comercial":"false",
"publication_date":"2018-11-07",
"_version_":"1718206093473087488",
"pdf_url":"http://books.scielo.org//id/23pcw/pdf/farias-9788574554907.pdf",
"pdf_filename":"farias-9788574554907.pdf",
"metadata_filename":"farias-9788574554907.json",
"text":"..."
}
```
### Data Fields
All fields are of string type except `year`.
### Data Splits
All records are in the default `train` split.
## Dataset Creation
### Curation Rationale
Part of the big science efforts to create lanague modeling datasets.
### Source Data
[scielo.org](https://search.livros.scielo.org/search/?fb=&where=BOOK&filter%5Bis_comercial_filter%5D%5B%5D=f)
#### Initial Data Collection and Normalization
All PDFs are directly downloaded from the website and text is extracted with [pdftotext](https://pypi.org/project/pdftotext/) lib.
#### Who are the source language producers?
NA
### Annotations
No annotation is available.
#### Annotation process
NA
#### Who are the annotators?
NA
### Personal and Sensitive Information
NA
## Considerations for Using the Data
### Social Impact of Dataset
NA
### Discussion of Biases
NA
### Other Known Limitations
NA
## Additional Information
### Dataset Curators
[@chenghao](https://huggingface.co/chenghao)
### Licensing Information
Provide the license and link to the license webpage if available.
[CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/)
### Contributions
NA | [
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collectivat/tv3_parla | collectivat | 2022-12-12T09:01:48Z | 301 | 3 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:text-generation",
"task_ids:language-modeling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ca",
"license:cc-by-nc-4.0",
"region:us"
] | 2022-12-12T09:01:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ca
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- automatic-speech-recognition
- text-generation
task_ids:
- language-modeling
pretty_name: TV3Parla
---
# Dataset Card for TV3Parla
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://collectivat.cat/asr#tv3parla
- **Repository:**
- **Paper:** [Building an Open Source Automatic Speech Recognition System for Catalan](https://www.isca-speech.org/archive/iberspeech_2018/kulebi18_iberspeech.html)
- **Point of Contact:** [Col·lectivaT](mailto:info@collectivat.cat)
### Dataset Summary
This corpus includes 240 hours of Catalan speech from broadcast material.
The details of segmentation, data processing and also model training are explained in Külebi, Öktem; 2018.
The content is owned by Corporació Catalana de Mitjans Audiovisuals, SA (CCMA);
we processed their material and hereby making it available under their terms of use.
This project was supported by the Softcatalà Association.
### Supported Tasks and Leaderboards
The dataset can be used for:
- Language Modeling.
- Automatic Speech Recognition (ASR) transcribes utterances into words.
### Languages
The dataset is in Catalan (`ca`).
## Dataset Structure
### Data Instances
```
{
'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'audio': {'path': 'tv3_0.3/wav/train/5662515_1492531876710/5662515_1492531876710_120.180_139.020.wav',
'array': array([-0.01168823, 0.01229858, 0.02819824, ..., 0.015625 ,
0.01525879, 0.0145874 ]),
'sampling_rate': 16000},
'text': 'algunes montoneres que que et feien anar ben col·locat i el vent també hi jugava una mica de paper bufava vent de cantó alguns cops o de cul i el pelotón el vent el porta molt malament hi havia molts nervis'
}
```
### Data Fields
- `path` (str): Path to the audio file.
- `audio` (dict): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling
rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and
resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might
take a significant amount of time. Thus, it is important to first query the sample index before the `"audio"` column,
*i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- `text` (str): Transcription of the audio file.
### Data Splits
The dataset is split into "train" and "test".
| | train | test |
|:-------------------|-------:|-----:|
| Number of examples | 159242 | 2220 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{kulebi18_iberspeech,
author={Baybars Külebi and Alp Öktem},
title={{Building an Open Source Automatic Speech Recognition System for Catalan}},
year=2018,
booktitle={Proc. IberSPEECH 2018},
pages={25--29},
doi={10.21437/IberSPEECH.2018-6}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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crystina-z/msmarco-passage | crystina-z | 2022-02-28T19:13:53Z | 301 | 1 | null | [
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jhonparra18/spanish_billion_words_clean | jhonparra18 | 2022-01-27T04:27:24Z | 301 | 4 | null | [
"region:us"
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ashraq/movielens_ratings | ashraq | 2022-06-29T17:29:31Z | 301 | 2 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jonathan-roberts1/SIRI-WHU | jonathan-roberts1 | 2023-03-31T17:18:08Z | 301 | 1 | null | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
] | 2023-03-31T17:18:08Z | 2023-01-20T15:46:58.000Z | 2023-01-20T15:46:58 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': agriculture
'1': commercial
'2': harbor
'3': idle_land
'4': industrial
'5': meadow
'6': overpass
'7': park
'8': pond
'9': residential
'10': river
'11': water
splits:
- name: train
num_bytes: 158215614.4
num_examples: 2400
download_size: 147702566
dataset_size: 158215614.4
license: other
task_categories:
- image-classification
- zero-shot-image-classification
---
# Dataset Card for "SIRI-WHU"
## Dataset Description
- **Paper** [Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
- **Paper** [The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
- **Paper** [Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
### Licensing Information
CC BY-NC-ND
## Citation Information
[Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/36/4358825/07329997.pdf)
[The Fisher kernel coding framework for high spatial resolution scene classification](https://www.mdpi.com/2072-4292/8/2/157/pdf)
[Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery](https://ieeexplore.ieee.org/iel7/8859/7473942/07466064.pdf)
```
@article{zhao2015dirichlet,
title={Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery},
author={Zhao, Bei and Zhong, Yanfei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={54},
number={4},
pages={2108--2123},
year={2015},
publisher={IEEE}
}
@article{zhao2016fisher,
title={The Fisher kernel coding framework for high spatial resolution scene classification},
author={Zhao, Bei and Zhong, Yanfei and Zhang, Liangpei and Huang, Bo},
journal={Remote Sensing},
volume={8},
number={2},
pages={157},
year={2016},
publisher={MDPI}
}
@article{zhu2016bag,
title={Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery},
author={Zhu, Qiqi and Zhong, Yanfei and Zhao, Bei and Xia, Gui-Song and Zhang, Liangpei},
journal={IEEE Geoscience and Remote Sensing Letters},
volume={13},
number={6},
pages={747--751},
year={2016},
publisher={IEEE}
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
pawlo2013/one_piece_dataset | pawlo2013 | 2023-10-28T13:45:08Z | 301 | 0 | null | [
"region:us"
] | 2023-10-28T13:45:08Z | 2023-10-27T06:34:37.000Z | 2023-10-27T06:34:37 | ---
dataset_info:
features:
- name: full_colour
dtype: image
- name: sketch
dtype: image
splits:
- name: train
num_bytes: 170204480.0
num_examples: 922
download_size: 170225532
dataset_size: 170204480.0
---
# Dataset Card for "one_piece_dataset"
<h1> This dataset contains 922 images taken from the one piece anime, with each row containing the coloured image and the sketch one. </h1>
<img alt='example images' src="./combined.png"/ >
<h2> Example Setup (the images are not normalized) </h2>
<code>
transform = Compose([
transforms.Resize((128, 128)),
transforms.ToTensor(),
transforms.Lambda(lambda t: (t * 2) - 1)
])
def train():
def transforms(examples):
examples["sketch_pixel_values"] = [transform(image.convert("RGB")) for image in examples["sketch"]]
examples["full_colour_pixel_values"] = [ transform(image.convert("RGB")) for image in examples["full_colour"]]
del examples["sketch"]
del examples["full_colour"]
return examples
dataset = load_dataset("pawlo2013/one_piece_dataset", split="train")
transformed_full_colour_dataset = dataset.with_transform(transforms)
dataloader = DataLoader(transformed_full_colour_dataset, batch_size=16, shuffle=True, num_workers=0)
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swahili | null | 2022-11-18T21:49:35Z | 300 | 7 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:sw",
"license:cc-by-4.0",
"region:us"
] | 2022-11-18T21:49:35Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- sw
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- language-modeling
- masked-language-modeling
paperswithcode_id: null
pretty_name: swahili
dataset_info:
features:
- name: text
dtype: string
config_name: swahili
splits:
- name: train
num_bytes: 7700136
num_examples: 42069
- name: test
num_bytes: 695092
num_examples: 3371
- name: validation
num_bytes: 663520
num_examples: 3372
download_size: 2783330
dataset_size: 9058748
---
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Repository:**
- **Paper:** https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7339006/
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
The Swahili dataset developed specifically for language modeling task.
The dataset contains 28,000 unique words with 6.84M, 970k, and 2M words for the train,
valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and,
the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modeling.
### Supported Tasks and Leaderboards
Language Modeling
### Languages
Swahili (sw)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- text : A line of text in Swahili
### Data Splits
train = 80%, valid = 10%, test = 10%
## Dataset Creation
### Curation Rationale
Enhancing African low-resource languages
### Source Data
#### Initial Data Collection and Normalization
The dataset contains 28,000 unique words with 6.84 M, 970k, and 2 M words for the train, valid and test partitions respectively which represent the ratio 80:10:10.
The entire dataset is lowercased, has no punctuation marks and, the start and end of sentence markers have been incorporated to facilitate easy tokenization during language modelling.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
Unannotated data
#### Who are the annotators?
NA
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
Enhancing African low-resource languages
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
"""\
@InProceedings{huggingface:dataset,
title = Language modeling data for Swahili (Version 1),
authors={Shivachi Casper Shikali, & Mokhosi Refuoe.
},
year={2019},
link = http://doi.org/10.5281/zenodo.3553423
}
"""
### Contributions
Thanks to [@akshayb7](https://github.com/akshayb7) for adding this dataset. | [
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yoruba_gv_ner | null | 2023-01-25T15:03:39Z | 300 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:yo",
"license:cc-by-3.0",
"region:us"
] | 2023-01-25T15:03:39Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- yo
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Yoruba GV NER Corpus
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-PER
'2': I-PER
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-DATE
'8': I-DATE
config_name: yoruba_gv_ner
splits:
- name: train
num_bytes: 358885
num_examples: 817
- name: validation
num_bytes: 50161
num_examples: 117
- name: test
num_bytes: 96518
num_examples: 237
download_size: 254347
dataset_size: 505564
---
# Dataset Card for Yoruba GV NER Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER)
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
- **Leaderboard:**
- **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de)
### Dataset Summary
The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Yorùbá.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-LOC, 0, 0, 0, 0],
'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and dates & times (DATE). (O) is used for tokens not considered part of any named entity.
### Data Splits
Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens)
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Yorùbá.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/).
[More Information Needed]
#### Who are the source language producers?
The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated by Jesujoba Alabi and David Adelani for the paper:
[Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/).
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The annotated data sets were developed by students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Alvenir/nst-da-16khz | Alvenir | 2021-11-29T08:58:25Z | 300 | 1 | null | [
"region:us"
] | 2021-11-29T08:58:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # NST Danish 16kHz dataset from Sprakbanken
Data is from sprakbanken and can be accessed using following [link](https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-19/).
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DDSC/reddit-da-asr-preprocessed | DDSC | 2022-02-15T19:17:08Z | 300 | 0 | null | [
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GEM/RiSAWOZ | GEM | 2022-10-24T15:30:01Z | 300 | 5 | null | [
"task_categories:conversational",
"annotations_creators:crowd-sourced",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:cc-by-4.0",
"dialog-response-generation",
"region:us"
] | 2022-10-24T15:30:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowd-sourced
language_creators:
- unknown
language:
- zh
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: RiSAWOZ
tags:
- dialog-response-generation
---
# Dataset Card for GEM/RiSAWOZ
## Dataset Description
- **Homepage:** https://terryqj0107.github.io/RiSAWOZ_webpage
- **Repository:** https://github.com/terryqj0107/RiSAWOZ
- **Paper:** https://aclanthology.org/2020.emnlp-main.67
- **Leaderboard:** N/A
- **Point of Contact:** Deyi Xiong
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/RiSAWOZ).
### Dataset Summary
RiSAWOZ is a Chinese dialog dataset. It can be used to study various dialogue tasks, such as Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/RiSAWOZ')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/RiSAWOZ).
#### website
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### paper
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.67)
#### authors
Jun Quan (Soochow University, Suzhou, China), Shian Zhang (Soochow University, Suzhou, China), Qian Cao(Soochow University, Suzhou, China), Zizhong Li (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/terryqj0107/RiSAWOZ)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.emnlp-main.67)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{quan-etal-2020-risawoz,
title = "{R}i{SAWOZ}: A Large-Scale Multi-Domain {W}izard-of-{O}z Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling",
author = "Quan, Jun and
Zhang, Shian and
Cao, Qian and
Li, Zizhong and
Xiong, Deyi",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.67",
pages = "930--940",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Deyi Xiong
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
dyxiong@tju.edu.cn
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
Only Mandarin Chinese is covered in this dataset.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Mandarin Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
RiSAWOZ can be used to support the study under various dialogue tasks, such as Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution and Unified Generative Ellipsis and Coreference Resolution.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate system response given dialogue context across multiple domains.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Soochow University and Tianjin University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Jun Quan (Soochow University, Suzhou, China), Shian Zhang (Soochow University, Suzhou, China), Qian Cao(Soochow University, Suzhou, China), Zizhong Li (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
the National Key Research and Development Project
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Tianhao Shen (Tianjin University, Tianjin, China), Chaobin You (Tianjin University, Tianjin, China), Deyi Xiong (Tianjin University, Tianjin, China)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- gem_id (string): GEM-RiSAWOZ-{split}-{id}
- dialogue_id (string): dialogue ID
- goal (string): natural language descriptions of the user goal
- domains (list of strings): domains mentioned in current dialogue session
- dialogue (list of dicts): dialog turns and corresponding annotations. Each turn includes:
- turn_id (int): turn ID
- turn_domain (list of strings): domain mentioned in current turn
- user_utterance (string): user utterance
- system_utterance (string): system utterance
- belief_state (dict): dialogue state, including:
- inform slot-values (dict): the slots and corresponding values informed until current turn
- turn_inform (dict): the slots and corresponding values informed in current turn
- turn request (dict): the slots requested in current turn
- user_actions (list of lists): user dialogue acts in current turn
- user_actions (list of lists): system dialogue acts in current turn
- db_results (list of strings): database search results
- segmented_user_utterance (string): word segmentation result of user utterance
- segmented_system_utterance (string): word segmentation result of system utterance
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
[
{
"dialogue_id": "attraction_goal_4-63###6177",
"goal": "attraction_goal_4-63: 你是苏州人，但不怎么出去玩。你朋友来苏州找你，你准备带他逛逛“水乡古镇”，你希望客服给你推荐个消费水平“中等”的地方。然后你要问清楚这地方“是否地铁直达”、“特点”、“门票价格”这些信息。最后，你要感谢客服的帮助，然后说再见。",
"domains": [
"旅游景点"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，我是苏州人，但是不怎么出去玩，我朋友来苏州找我了，我准备带他逛逛水乡古镇，你能帮我推荐一下吗？",
"system_utterance": "推荐您去周庄古镇。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇"
},
"turn_inform": {
"旅游景点-景点类型": "水乡 古镇"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"景点类型",
"水乡 古镇"
],
[
"Greeting",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Recommend",
"旅游景点",
"名称",
"周庄 古镇"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为8",
"{'名称': '周庄古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '100元', '电话号码': '400-8282900', '地址': '苏州市昆山市周庄镇全福路43号', '评分': 4.5, '开放时间': '07:30-18:50', '特点': '小桥流水与人家，双桥水巷摇橹船，还有沈万三的足迹待你寻访'}",
"{'名称': '同里古镇', '区域': '吴江', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '100元', '电话号码': '0512-63325728', '地址': '苏州市吴江区同里古镇', '评分': 4.5, '开放时间': '07:30-17:30', '特点': '五湖环抱的江南水乡古镇，走三桥，参观小巧精致的园林退思园。'}",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}",
"{'名称': '平江路', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-69163907', '地址': '江苏省苏州市姑苏区白塔东路65号', '评分': 4.5, '开放时间': '全天', '特点': '漫步保存完整的古街区，看小桥流水古宅，逛文艺小店，吃美食。'}",
"{'名称': '木渎古镇', '区域': '吴中区', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-66514042', '地址': '苏州市吴中区木渎镇山塘街188号', '评分': 4.4, '开放时间': '08:30-17:00', '特点': '以园林为特色的古镇，游严家花园等古典园林，坐船看小桥流水。'}",
"{'名称': '甪直古镇', '区域': '吴中区', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-66191668', '地址': '苏州市吴中区甪直镇晓市路21号', '评分': 4.3, '开放时间': '07:30-17:30', '特点': '甪直古镇有2500多年历史，甪直境内水流纵横，桥梁密布，有五湖之厅、六泽之冲之称。'}",
"{'名称': '千灯古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '便宜', '是否地铁直达': '否', '门票价格': '免费', '电话号码': '0512-57472155', '地址': '苏州市昆山市千灯古镇尚书路1号', '评分': 4.3, '开放时间': '08:00-17:00', '特点': '千灯古镇，距今已有2500多年的历史，古镇白墙黑瓦，昆韵盎然。'}",
"{'名称': '锦溪古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '中等', '是否地铁直达': '否', '门票价格': '65元', '电话号码': '0512-57224669', '地址': '苏州市昆山市锦溪镇邵甸港路18号', '评分': 4.4, '开放时间': '08:00-17:00', '特点': '锦溪古镇位于昆山南郊的淀山湖畔，是一座有千年历史的江南水乡。'}"
],
"segmented_user_utterance": "你好 ， 我 是 苏州人 ， 但是 不怎么 出去玩 ， 我 朋友 来 苏州 找 我 了 ， 我 准备 带 他 逛逛 水乡 古镇 ， 你 能 帮 我 推荐 一下 吗 ？",
"segmented_system_utterance": "推荐 您 去 周庄 古镇 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "我希望这个地方消费水平适中的，这个是中等的吗？",
"system_utterance": "这个不是，我又重新帮您找了一家山塘街。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等"
},
"turn_inform": {
"旅游景点-消费": "中等"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"消费",
"中等"
]
],
"system_actions": [
[
"Recommend",
"旅游景点",
"名称",
"山塘 街"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为3",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}",
"{'名称': '平江路', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-69163907', '地址': '江苏省苏州市姑苏区白塔东路65号', '评分': 4.5, '开放时间': '全天', '特点': '漫步保存完整的古街区，看小桥流水古宅，逛文艺小店，吃美食。'}",
"{'名称': '锦溪古镇', '区域': '昆山', '景点类型': '水乡古镇', '最适合人群': '朋友出游', '消费': '中等', '是否地铁直达': '否', '门票价格': '65元', '电话号码': '0512-57224669', '地址': '苏州市昆山市锦溪镇邵甸港路18号', '评分': 4.4, '开放时间': '08:00-17:00', '特点': '锦溪古镇位于昆山南郊的淀山湖畔，是一座有千年历史的江南水乡。'}"
],
"segmented_user_utterance": "我 希望 这个 地方 消费水平 适中 的 ， 这个 是 中等 的 吗 ？",
"segmented_system_utterance": "这个 不是 ， 我 又 重新 帮 您 找 了 一家 山塘 街 。"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "听这地方应该挺好玩，他们家特点是什么？",
"system_utterance": "特点是欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {
"旅游景点-名称": "山塘 街"
},
"turn request": [
"特点"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"山塘 街"
],
[
"Request",
"旅游景点",
"特点",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"特点",
"欣赏 苏州 老城 风貌 ， 参观 会馆 等 古迹 ， 乘 游船 ， 拍摄 美丽 的 夜景"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "听 这 地方 应该 挺好玩 ， 他们 家 特点 是 什么 ？",
"segmented_system_utterance": "特点 是 欣赏 苏州 老城 风貌 ， 参观 会馆 等 古迹 ， 乘 游船 ， 拍摄 美丽 的 夜景 。"
},
{
"turn_id": 3,
"turn_domain": [
"旅游景点"
],
"user_utterance": "听起来很不错，他们家门票是多少？",
"system_utterance": "门票免费。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": [
"门票价格"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"门票价格",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"门票价格",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "听 起来 很 不错 ， 他们 家 门票 是 多少 ？",
"segmented_system_utterance": "门票 免费 。"
},
{
"turn_id": 4,
"turn_domain": [
"旅游景点"
],
"user_utterance": "还行，那他们家是否有地铁直达呢？",
"system_utterance": "嗯嗯，可以地铁直达。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"是否地铁直达",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"是否地铁直达",
"是"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '山塘街', '区域': '姑苏区', '景点类型': '水乡古镇', '最适合人群': '情侣约会', '消费': '中等', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '0512-65314467', '地址': '苏州市姑苏区古城西北', '评分': 4.5, '开放时间': '08:30-21:00', '特点': '欣赏苏州老城风貌，参观会馆等古迹，乘游船，拍摄美丽的夜景。'}"
],
"segmented_user_utterance": "还 行 ， 那 他们 家 是否 有 地铁 直达 呢 ？",
"segmented_system_utterance": "嗯 嗯 ， 可以 地铁 直达 。"
},
{
"turn_id": 5,
"turn_domain": [
"通用"
],
"user_utterance": "谢谢你的帮助，再见！",
"system_utterance": "再见。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "水乡 古镇",
"旅游景点-消费": "中等",
"旅游景点-名称": "山塘 街"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "谢谢 你 的 帮助 ， 再见 ！",
"segmented_system_utterance": "再见 。"
}
]
},
{
"dialogue_id": "attraction_goal_3-33_v2###7307",
"goal": "attraction_goal_3-33: 你和朋友一起来苏州出差，顺便想去“苏州乐园森林水世界”逛一逛。因此，你要询问客服这个地方“是否地铁直达”、“评分”、“特点”这些信息。最后，你向客服表示感谢，结束对话。",
"domains": [
"旅游景点"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，我和朋友一起来苏州出差，顺便想去苏州乐园森林水世界玩玩，能否帮我看看这个地方是否有地铁直达呢？",
"system_utterance": "稍等，很抱歉，没有地铁能直接到这个景点呢。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"苏州 乐园 森林 水 世界"
],
[
"Request",
"旅游景点",
"是否地铁直达",
""
],
[
"Greeting",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"是否地铁直达",
"否"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "你好 ， 我 和 朋友 一 起来 苏州 出差 ， 顺便 想 去 苏州 乐园 森林 水 世界 玩玩 ， 能否 帮 我 看看 这个 地方 是否 有 地铁 直达 呢 ？",
"segmented_system_utterance": "稍 等 ， 很 抱歉 ， 没有 地铁 能 直接 到 这个 景点 呢 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你再帮我看看景点的评分好吗？",
"system_utterance": "这个景点的评分是4.4分。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": [
"评分"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"评分",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"评分",
"4.4"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "你 再 帮 我 看看 景点 的 评分 好 吗 ？",
"segmented_system_utterance": "这个 景点 的 评分 是 4.4 分 。"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "这个景点有啥特点呢？",
"system_utterance": "这个景点的特点是各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": [
"特点"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"特点",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"特点",
"各项 水上 娱乐 项目 皆 具 森林 特色 ， 被 赋予 森林 主题 ， 让 人 身处 浩瀚 森林 又 能 享受 到 海洋 般的 舒爽 水 游乐 。"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '苏州乐园森林水世界', '区域': '高新区', '景点类型': '游乐场', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '198元', '电话号码': '0512-68717107', '地址': '苏州市高新区阳山东路以西，山神湾路以东', '评分': 4.4, '开放时间': '09:00-17:00', '特点': '各项水上娱乐项目皆具森林特色，被赋予森林主题，让人身处浩瀚森林又能享受到海洋般的舒爽水游乐。'}"
],
"segmented_user_utterance": "这个 景点 有 啥 特点 呢 ？",
"segmented_system_utterance": "这个 景点 的 特点 是 各项 水上 娱乐 项目 皆 具 森林 特色 ， 被 赋予 森林 主题 ， 让 人 身处 浩瀚 森林 又 能 享受 到 海洋 般的 舒爽 水 游乐 。"
},
{
"turn_id": 3,
"turn_domain": [
"通用"
],
"user_utterance": "太好了，我觉得我会很喜欢这个地方，哈哈。",
"system_utterance": "这个景点挺不错的，就是没有地铁需要您乘坐其它交通工具了。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "太好了 ， 我 觉得 我会 很 喜欢 这个 地方 ， 哈哈 。",
"segmented_system_utterance": "这个 景点 挺不错 的 ， 就是 没有 地铁 需要 您 乘坐 其它 交通工具 了 。"
},
{
"turn_id": 4,
"turn_domain": [
"通用"
],
"user_utterance": "我打出租车去吧，就是多花点车费，没事。",
"system_utterance": "请问您还有其他问题吗？",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "我 打 出租车 去 吧 ， 就是 多花点 车费 ， 没事 。",
"segmented_system_utterance": "请问 您 还有 其他 问题 吗 ？"
},
{
"turn_id": 5,
"turn_domain": [
"通用"
],
"user_utterance": "没有了。谢谢你，再见。",
"system_utterance": "再见。",
"belief_state": {
"inform slot-values": {
"旅游景点-名称": "苏州 乐园 森林 水 世界"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "没有 了 。 谢谢 你 ， 再见 。",
"segmented_system_utterance": "再见 。"
}
]
}
]
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train:
- dialogues: 10000
- turns: 134580
- Avg. turns: 13.5
- Valid:
- dialogues: 600
- turns: 8116
- Avg. turns: 13.5
- Test:
- dialogues: 600
- turn: 9286
- Avg. turns: 15.5
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
the most complex example (i.e. with the most domains):
```
{
"dialogue_id": "attraction_restaurant_hotel_goal_2-69_v2###8355",
"goal": "attraction_restaurant_hotel_goal_2-69: 公司派你去苏州出差，你准备抽空旅游一下。第一，你想去一个“山水景区”，消费水平最好是“偏贵”的，你需要问清楚这地方“开放时间”、“门票价格”这些信息。第二，你要找个地方住，让客服给你推荐一家价位“中等”的“商务出行”酒店，并且询问这家酒店的“停车场”、“电话号码”这些信息。第三，你还想拔草美食，问客服有没有价位“中等”的“江浙菜”餐厅推荐，然后了解这家餐厅的“是否地铁直达”、“人均消费”这些信息。最后你要感谢客服的帮助，并说再见。",
"domains": [
"旅游景点",
"餐厅",
"酒店"
],
"dialogue": [
{
"turn_id": 0,
"turn_domain": [
"旅游景点"
],
"user_utterance": "你好，来苏州出差，准备抽空旅游一下。我想去山水景区，消费水平偏贵点，帮忙推荐一个。",
"system_utterance": "你好，向您推荐金鸡湖景区，感受一下苏州现代化的一面。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵"
},
"turn_inform": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"旅游景点",
"景点类型",
"山水 景区"
],
[
"Inform",
"旅游景点",
"消费",
"偏贵"
],
[
"Greeting",
"旅游景点",
"",
""
],
[
"General",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Greeting",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
],
[
"Recommend",
"旅游景点",
"名称",
"金鸡湖 景区"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}",
"{'名称': '沙家浜风景区', '区域': '常熟', '景点类型': '山水景区', '最适合人群': '家庭亲子', '消费': '偏贵', '是否地铁直达': '否', '门票价格': '110元', '电话号码': '0512-52500000', '地址': '苏州市常熟市沙家浜镇', '评分': 4.5, '开放时间': '08:00-16:30', '特点': '京剧样板戏《沙家浜》的发源地，访剧中场景，坐船深入芦苇荡。'}"
],
"segmented_user_utterance": "你好 ， 来 苏州 出差 ， 准备 抽空 旅游 一下 。 我 想 去 山水 景区 ， 消费水平 偏 贵点 ， 帮忙 推荐 一个 。",
"segmented_system_utterance": "你好 ， 向 您 推荐 金鸡湖 景区 ， 感受一下 苏州 现代化 的 一面 。"
},
{
"turn_id": 1,
"turn_domain": [
"旅游景点"
],
"user_utterance": "这里开放时间？需要安排一下。",
"system_utterance": "景区是全天开放的，可以尽情游览！",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区"
},
"turn_inform": {
"旅游景点-名称": "金鸡湖 景区"
},
"turn request": [
"开放时间"
]
},
"user_actions": [
[
"Inform",
"旅游景点",
"名称",
"金鸡湖 景区"
],
[
"Request",
"旅游景点",
"开放时间",
""
],
[
"General",
"旅游景点",
"",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"开放时间",
"全天"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}"
],
"segmented_user_utterance": "这里 开放 时间 ？ 需要 安排 一下 。",
"segmented_system_utterance": "景区 是 全天 开放 的 ， 可以 尽情 游览 ！"
},
{
"turn_id": 2,
"turn_domain": [
"旅游景点"
],
"user_utterance": "门票多少钱？",
"system_utterance": "这里是免费景区。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区"
},
"turn_inform": {},
"turn request": [
"门票价格"
]
},
"user_actions": [
[
"Request",
"旅游景点",
"门票价格",
""
]
],
"system_actions": [
[
"Inform",
"旅游景点",
"门票价格",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '金鸡湖景区', '区域': '工业园区', '景点类型': '山水景区', '最适合人群': '情侣约会', '消费': '偏贵', '是否地铁直达': '是', '门票价格': '免费', '电话号码': '400-7558558', '地址': '苏州市工业园区星港街158号', '评分': 4.5, '开放时间': '全天', '特点': '看东方之门等高楼，坐摩天轮，乘船夜游，感受苏州现代化的一面。'}"
],
"segmented_user_utterance": "门票 多少 钱 ？",
"segmented_system_utterance": "这里 是 免费 景区 。"
},
{
"turn_id": 3,
"turn_domain": [
"酒店"
],
"user_utterance": "哦，好的。还有酒店，帮我推荐个价位中等的商务酒店！",
"system_utterance": "推荐苏州金鸡湖李公堤瑞贝庭公寓酒店，在金鸡湖景区附近。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行"
},
"turn_inform": {
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"酒店",
"价位",
"中等"
],
[
"Inform",
"酒店",
"酒店类型",
"商务 出行"
],
[
"General",
"酒店",
"",
""
]
],
"system_actions": [
[
"Recommend",
"酒店",
"名称",
"苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
],
[
"General",
"通用",
"",
""
]
],
"db_results": [
"数据库检索结果：成功匹配个数为16",
"{'名称': '苏州慢享主题酒店', '区域': '姑苏区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '266元', '地址': '苏州姑苏区景德路26-64号', '电话号码': '0512-67570999', '评分': 4.7}",
"{'名称': '苏州慢享主题酒店', '区域': '姑苏区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '278元', '地址': '苏州姑苏区景德路26-64号', '电话号码': '0512-67570999', '评分': 4.7}",
"{'名称': '美锦酒店', '区域': '高新区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '308元', '地址': '苏州高新区滨河路999号花样年喜年生活广场5栋1层', '电话号码': '0512-66053331', '评分': 4.8}",
"{'名称': '美锦酒店', '区域': '高新区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '349元', '地址': '苏州高新区滨河路999号花样年喜年生活广场5栋1层', '电话号码': '0512-66053331', '评分': 4.8}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州途乐酒店公寓', '区域': '工业园区', '星级': '2', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '486元', '地址': '苏州工业园区苏州丰隆城市中心T1楼', '电话号码': '151-5149-7911', '评分': 4.6}",
"{'名称': '苏州途乐酒店公寓', '区域': '工业园区', '星级': '2', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '486元', '地址': '苏州工业园区苏州丰隆城市中心T1楼', '电话号码': '151-5149-7911', '评分': 4.6}",
"{'名称': '万悦酒店', '区域': '吴中区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '346元', '地址': '苏州吴中区金山路47-2号', '电话号码': '0512-83808380', '评分': 4.5}",
"{'名称': '万悦酒店', '区域': '吴中区', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '338元', '地址': '苏州吴中区金山路47-2号', '电话号码': '0512-83808380', '评分': 4.5}",
"{'名称': '周庄多瓦台临河客栈', '区域': '昆山', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '收费', '房费': '279元', '地址': '昆山周庄镇东浜村75号', '电话号码': '181-3619-1632', '评分': 4.8}",
"{'名称': '周庄多瓦台临河客栈', '区域': '昆山', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '收费', '房费': '279元', '地址': '昆山周庄镇东浜村75号', '电话号码': '181-3619-1632', '评分': 4.8}",
"{'名称': '常熟长江路亚朵酒店', '区域': '常熟', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '312元', '地址': '常熟长江路268号', '电话号码': '0512-51661666', '评分': 4.8}",
"{'名称': '常熟长江路亚朵酒店', '区域': '常熟', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '312元', '地址': '常熟长江路268号', '电话号码': '0512-51661666', '评分': 4.8}",
"{'名称': '维也纳酒店', '区域': '常熟', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '322元', '地址': '常熟碧溪镇金港路8号', '电话号码': '0512-52018188', '评分': 4.8}",
"{'名称': '维也纳酒店', '区域': '常熟', '星级': '3', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '340元', '地址': '常熟碧溪镇金港路8号', '电话号码': '0512-52018188', '评分': 4.8}"
],
"segmented_user_utterance": "哦 ， 好 的 。 还有 酒店 ， 帮 我 推荐 个 价位 中等 的 商务酒店 ！",
"segmented_system_utterance": "推荐 苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店 ， 在 金鸡湖 景区 附近 。"
},
{
"turn_id": 4,
"turn_domain": [
"酒店"
],
"user_utterance": "你想得真周到!这家酒店有停车场吗？",
"system_utterance": "酒店配备有一个免费的停车场。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn request": [
"停车场"
]
},
"user_actions": [
[
"Inform",
"酒店",
"名称",
"苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
],
[
"Request",
"酒店",
"停车场",
""
]
],
"system_actions": [
[
"Inform",
"酒店",
"停车场",
"免费"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}"
],
"segmented_user_utterance": "你 想 得 真 周到 ! 这家 酒店 有 停车场 吗 ？",
"segmented_system_utterance": "酒店 配备 有 一个 免费 的 停车场 。"
},
{
"turn_id": 5,
"turn_domain": [
"酒店"
],
"user_utterance": "酒店的电话告诉我一下。",
"system_utterance": "电话号码：0512-69995666。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": [
"电话号码"
]
},
"user_actions": [
[
"Request",
"酒店",
"电话号码",
""
]
],
"system_actions": [
[
"Inform",
"酒店",
"电话号码",
"0512 - 69995666"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为2",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '大床房', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}",
"{'名称': '苏州金鸡湖李公堤瑞贝庭公寓酒店', '区域': '工业园区', '星级': '4', '价位': '中等', '酒店类型': '商务出行', '房型': '标准间', '停车场': '免费', '房费': '438元', '地址': '苏州工业园区李公堤三期E区商业街9幢', '电话号码': '0512-69995666', '评分': 4.6}"
],
"segmented_user_utterance": "酒店 的 电话 告诉 我 一下 。",
"segmented_system_utterance": "电话号码 ： 0512 - 69995666 。"
},
{
"turn_id": 6,
"turn_domain": [
"餐厅"
],
"user_utterance": "嗯，我还想拔草美食。有没有价位中等的江浙菜餐厅推荐一个。",
"system_utterance": "推荐老枫庭。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等"
},
"turn request": []
},
"user_actions": [
[
"Inform",
"餐厅",
"菜系",
"江浙 菜"
],
[
"Inform",
"餐厅",
"价位",
"中等"
],
[
"General",
"餐厅",
"",
""
]
],
"system_actions": [
[
"Recommend",
"餐厅",
"名称",
"老枫庭"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为3",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}",
"{'名称': '同里老家', '区域': '吴江', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '87元', '地址': '同里镇环湖西路318号', '电话号码': '0512-63339777', '评分': 4.5, '营业时间': '周一至周日 10:00-22:00', '推荐菜': '太湖三白'}",
"{'名称': '周庄花间堂桔梗餐厅', '区域': '昆山', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '98元', '地址': '周庄镇中市街110号景点迷楼北10米', '电话号码': '138-1289-9961', '评分': 4.5, '营业时间': '周一至周日 07:00-20:30', '推荐菜': '银鱼炒蛋'}"
],
"segmented_user_utterance": "嗯 ， 我 还 想 拔草 美食 。 有没有 价位 中等 的 江浙 菜 餐厅 推荐 一个 。",
"segmented_system_utterance": "推荐 老枫庭 。"
},
{
"turn_id": 7,
"turn_domain": [
"餐厅"
],
"user_utterance": "这家餐厅地铁直达吗？",
"system_utterance": "抱歉，餐厅没有地铁直达!",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {
"餐厅-名称": "老枫庭"
},
"turn request": [
"是否地铁直达"
]
},
"user_actions": [
[
"Inform",
"餐厅",
"名称",
"老枫庭"
],
[
"Request",
"餐厅",
"是否地铁直达",
""
]
],
"system_actions": [
[
"Inform",
"餐厅",
"是否地铁直达",
"否"
],
[
"General",
"通用",
"",
""
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}"
],
"segmented_user_utterance": "这家 餐厅 地铁 直达 吗 ？",
"segmented_system_utterance": "抱歉 ， 餐厅 没有 地铁 直达 !"
},
{
"turn_id": 8,
"turn_domain": [
"餐厅"
],
"user_utterance": "人均消费呢？",
"system_utterance": "老枫庭人均消费在94元左右。",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": [
"人均消费"
]
},
"user_actions": [
[
"Request",
"餐厅",
"人均消费",
""
]
],
"system_actions": [
[
"Inform",
"餐厅",
"人均消费",
"老枫庭"
]
],
"db_results": [
"数据库检索结果：成功匹配个数为1",
"{'名称': '老枫庭', '区域': '吴中区', '菜系': '江浙菜', '价位': '中等', '是否地铁直达': '否', '人均消费': '94元', '地址': '木渎灵天路55号芭提雅休闲娱乐广场5号楼', '电话号码': '0512-66277557', '评分': 4.6, '营业时间': '周一至周日 11:00-13:30 17:00-20:30', '推荐菜': '松鼠桂鱼'}"
],
"segmented_user_utterance": "人均 消费 呢 ？",
"segmented_system_utterance": "老枫庭 人均 消费 在 94 元 左右 。"
},
{
"turn_id": 9,
"turn_domain": [
"通用"
],
"user_utterance": "好，所有问题都解决了，谢谢你的帮助！再见！",
"system_utterance": "不客气！这是我应该做的！再见！",
"belief_state": {
"inform slot-values": {
"旅游景点-景点类型": "山水 景区",
"旅游景点-消费": "偏贵",
"旅游景点-名称": "金鸡湖 景区",
"餐厅-菜系": "江浙 菜",
"餐厅-价位": "中等",
"餐厅-名称": "老枫庭",
"酒店-价位": "中等",
"酒店-酒店类型": "商务 出行",
"酒店-名称": "苏州 金鸡湖 李公堤 瑞贝庭 公寓 酒店"
},
"turn_inform": {},
"turn request": []
},
"user_actions": [
[
"Bye",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
]
],
"system_actions": [
[
"Bye",
"通用",
"",
""
],
[
"General",
"通用",
"",
""
]
],
"db_results": [],
"segmented_user_utterance": "好 ， 所有 问题 都 解决 了 ， 谢谢 你 的 帮助 ！ 再见 ！",
"segmented_system_utterance": "不 客气 ！ 这 是 我 应该 做 的 ！ 再见 ！"
}
]
}
```
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
RiSAWOZ is the first large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The corpus contains rich semantic annotations, such as ellipsis and coreference, in addition to traditional dialogue annotations (dialogue states, dialogue acts, etc.), which can be used in various tasks in dialogue system.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution, Unified Generative Ellipsis and Coreference Resolution
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Website](https://terryqj0107.github.io/RiSAWOZ_webpage)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- In task-oriented dialogue system, the Natural Language Understanding (NLU) module aims to convert the user utterance into the representation that computer can understand, which includes intent and dialogue act (slot & value) detection.
- Dialogue State Tracking (DST) is a core component in task-oriented dialogue systems, which extracts dialogue states (user goals) embedded in dialogue context. It has progressed toward open-vocabulary or generation-based DST where state-of-the-art models can generate dialogue states from dialogue context directly.
- Context-to-Text Generation: encoding dialogue context to decode system response.
- Coreference Resolution: predict coreference clusters where all mentions are referring to the same entity for each dialogue.
- Unified Generative Ellipsis and Coreference Resolution: generating omitted or referred expressions from the dialogue context.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Natural Language Understanding, Dialogue State Tracking, Dialogue Context-to-Text Generation, Coreference Resolution, Unified Generative Ellipsis and Coreference Resolution
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
- Natural Language Understanding:
- F1 score: F1 score of user intent.
- Dialogue State Tracking:
- Joint Accuracy: accuracy of turn-level dialogue states.
- Dialogue Context-to-Text Generation:
- inform rate: measures the percentage that the output contains the appropriate entity the user asks for.
- success rate: estimates the proportion that all the requested attributes have been answered.
- BLEU: the BLEU score of generated system response.
- Combined Score: (inform + success) ∗ 0.5 + BLEU as an overall
quality.
- Coreference Resolution:
- MUC F1 Score: a link-based metric. Mentions in the same entity/cluster are considered “linked”. MUC penalizes the missing links and incorrect links, each with the same weight.
- B3 F1 Score: a mention-based metric.The evaluation score depends on the fraction of the correct mentions included in the response entities (i.e. entities created by the system).
- CEAFφ4 F1 Score: a metric which assumes each key entity should only be mapped to one response entity, and vice versa. It aligns the key entities (clusters) with the response entities in the best way, and compute scores from that alignment.
- Average F1 Score: an average F1 score of the above three metrics.
- Unified Generative Ellipsis and Coreference Resolution:
- Exact Match Rate: measures whether the generated utterances exactly match the ground-truth utterances.
- BLEU: the BLEU score of generated utterances
- Resolution F1: comparing machine-generated words with ground-truth words only from the ellipsis/coreference part of user utterances.
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
see "Definitions of other metrics"
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
same as our dataset
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Joint Accuracy, Inform Rate, Success Rate, BLEU Score and Combined Score on MultiWOZ and CrossWOZ dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Gather human-to-human dialog in Chinese.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate system response given dialogue context across multiple domains.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Other crowdworker platform`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
domains: Attraction, Restaurant, Hotel, Flight, Train, Weather, Movie, TV, Computer, Car, Hospital, Courses
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Rule-based and manual selection criteria
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
crowd-sourced
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
51<n<100
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Chinese native speaker
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
3
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
3
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
- dialogue_id (string): dialogue ID
- goal (string): natural language descriptions of the user goal
- domains (list of strings): domains mentioned in current dialogue session
- turn_id (int): turn ID
- turn_domain (list of strings): domain mentioned in current turn
- belief_state (dict): dialogue state, including:
- inform slot-values (dict): the slots and corresponding values informed until current turn
- turn_inform (dict): the slots and corresponding values informed in current turn
- turn request (dict): the slots requested in current turn
- user_actions (list of lists): user dialogue acts in current turn
- user_actions (list of lists): system dialogue acts in current turn
- db_results (list of strings): database search results
- segmented_user_utterance (string): word segmentation result of user utterance
- segmented_system_utterance (string): word segmentation result of system utterance
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
unknown
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The slots and values as well as utterances do not contain any personal information.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
yes
#### Maintenance Plan Details
<!-- info: Describe the original dataset's maintenance plan. -->
<!-- scope: microscope -->
Building a leaderboard webpage to trace and display the latest results on the [dataset](https://terryqj0107.github.io/RiSAWOZ_webpage/)
#### Maintainer Contact Information
<!-- info: Provide contact information of a person responsible for the dataset maintenance -->
<!-- scope: periscope -->
Deyi Xiong (dyxiong@tju.edu.cn)
#### Any Contestation Mechanism?
<!-- info: Does the maintenance plan include a contestation mechanism allowing individuals to request removal fo content? -->
<!-- scope: periscope -->
contact maintainer
#### Contestation Form Link
<!-- info: Provide the form link or contact information -->
<!-- scope: periscope -->
Deyi Xiong (dyxiong@tju.edu.cn)
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
RiSAWOZ is the first large-scale multi-domain Chinese Wizard-of-Oz dataset with rich semantic annotations.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
yes
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
None
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Using the trained model on domains that are not included in the 12 domains selected for this dataset.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Designing models that leverage unknown bias in the dataset to optimize specific metrics.
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GEM/RotoWire_English-German | GEM | 2022-10-24T15:30:03Z | 300 | 2 | null | [
"task_categories:table-to-text",
"annotations_creators:automatically-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"language:de",
"license:cc-by-4.0",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
- de
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: RotoWire_English-German
tags:
- data-to-text
---
# Dataset Card for GEM/RotoWire_English-German
## Dataset Description
- **Homepage:** https://sites.google.com/view/wngt19/dgt-task
- **Repository:** https://github.com/neulab/dgt
- **Paper:** https://www.aclweb.org/anthology/D19-5601/
- **Leaderboard:** N/A
- **Point of Contact:** Hiroaki Hayashi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/RotoWire_English-German).
### Dataset Summary
This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/RotoWire_English-German')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/RotoWire_English-German).
#### website
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### paper
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### authors
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/neulab/dgt)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{hayashi-etal-2019-findings,
title = "Findings of the Third Workshop on Neural Generation and Translation",
author = "Hayashi, Hiroaki and
Oda, Yusuke and
Birch, Alexandra and
Konstas, Ioannis and
Finch, Andrew and
Luong, Minh-Thang and
Neubig, Graham and
Sudoh, Katsuhito",
booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5601",
doi = "10.18653/v1/D19-5601",
pages = "1--14",
abstract = "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Hiroaki Hayashi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
hiroakih@andrew.cmu.edu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`, `German`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Describe a basketball game given its box score table (and possibly a summary in a foreign language).
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Carnegie Mellon University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Graham Neubig
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Hiroaki Hayashi (Carnegie Mellon University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `id` (`string`): The identifier from the original dataset.
- `gem_id` (`string`): The identifier from GEMv2.
- `day` (`string`): Date of the game (Format: `MM_DD_YY`)
- `home_name` (`string`): Home team name.
- `home_city` (`string`): Home team city name.
- `vis_name` (`string`): Visiting (Away) team name.
- `vis_city` (`string`): Visiting team (Away) city name.
- `home_line` (`Dict[str, str]`): Home team statistics (e.g., team free throw percentage).
- `vis_line` (`Dict[str, str]`): Visiting team statistics (e.g., team free throw percentage).
- `box_score` (`Dict[str, Dict[str, str]]`): Box score table. (Stat_name to [player ID to stat_value].)
- `summary_en` (`List[string]`): Tokenized target summary in English.
- `sentence_end_index_en` (`List[int]`): Sentence end indices for `summary_en`.
- `summary_de` (`List[string]`): Tokenized target summary in German.
- `sentence_end_index_de` (`List[int]`): ): Sentence end indices for `summary_de`.
- (Unused) `detok_summary_org` (`string`): Original summary provided by RotoWire dataset.
- (Unused) `summary` (`List[string]`): Tokenized summary of `detok_summary_org`.
- (Unused) `detok_summary` (`string`): Detokenized (with organizer's detokenizer) summary of `summary`.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
- Structured data are directly imported from the original RotoWire dataset.
- Textual data (English, German) are associated to each sample.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'id': '11_02_16-Jazz-Mavericks-TheUtahJazzdefeatedthe',
'gem_id': 'GEM-RotoWire_English-German-train-0'
'day': '11_02_16',
'home_city': 'Utah',
'home_name': 'Jazz',
'vis_city': 'Dallas',
'vis_name': 'Mavericks',
'home_line': {
'TEAM-FT_PCT': '58', ...
},
'vis_line': {
'TEAM-FT_PCT': '80', ...
},
'box_score': {
'PLAYER_NAME': {
'0': 'Harrison Barnes', ...
}, ...
'summary_en': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
'sentence_end_index_en': [16, 52, 100, 137, 177, 215, 241, 256, 288],
'summary_de': ['Die', 'Utah', 'Jazz', 'besiegten', 'am', 'Mittwoch', 'in', 'der', ...],
'sentence_end_index_de': [19, 57, 107, 134, 170, 203, 229, 239, 266],
'detok_summary_org': "The Utah Jazz defeated the Dallas Mavericks 97 - 81 ...",
'detok_summary': "The Utah Jazz defeated the Dallas Mavericks 97-81 ...",
'summary': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train
- Validation
- Test
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
- English summaries are provided sentence-by-sentence to professional German translators with basketball knowledge to obtain sentence-level German translations.
- Split criteria follows the original RotoWire dataset.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
- The (English) summary length in the training set varies from 145 to 650 words, with an average of 323 words.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The use of two modalities (data, foreign text) to generate a document-level text summary.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The potential use of two modalities (data, foreign text) as input.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
- Translation
- Data-to-text verbalization
- Aggregation of the two above.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
- Added GEM ID in each sample.
- Normalize the number of players in each sample with "N/A" for consistent data loading.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Challenges in Data-to-Document Generation](https://aclanthology.org/D17-1239)
- [Data-to-Text Generation with Content Selection and Planning](https://ojs.aaai.org//index.php/AAAI/article/view/4668)
- [Findings of the Third Workshop on Neural Generation and Translation](https://aclanthology.org/D19-5601)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- Data-to-text
- Neural machine translation (NMT)
- Document-level generation and translation (DGT)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
- Textual accuracy towards the gold-standard summary.
- Content faithfulness to the input structured data.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `ROUGE`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Model-based measures proposed by (Wiseman et al., 2017):
- Relation Generation
- Content Selection
- Content Ordering
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
To evaluate the fidelity of the generated content to the input data.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
N/A.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
See Table 2 to 7 of (https://aclanthology.org/D19-5601) for previous results for this dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
A random subset of RotoWire dataset was chosen for German translation annotation.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
RotoWire
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Created for the dataset`
#### Creation Process
<!-- info: If created for the dataset, describe the creation process. -->
<!-- scope: microscope -->
Professional German language translators were hired to translate basketball summaries from a subset of RotoWire dataset.
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Translators are familiar with basketball terminology.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Basketball (NBA) game summaries.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Sentence-level translations were aligned back to the original English summary sentences.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Sentence-end indices for the tokenized summaries. Sentence boundaries can help users accurately identify aligned sentences in both languages, as well as allowing an accurate evaluation that involves sentence boundaries (ROUGE-L).
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated through automated script
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Token and number overlaps between pairs of aligned sentences are measured.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
Reusing by citing the original papers:
- Sam Wiseman, Stuart M. Shieber, Alexander M. Rush:
Challenges in Data-to-Document Generation. EMNLP 2017.
- Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh. Findings of the Third Workshop on Neural Generation and Translation. WNGT 2019.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
- English text in this dataset is from Rotowire, originally written by writers at Rotowire.com that are likely US-based.
- German text is produced by professional translators proficient in both English and German.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
- Structured data contain real National Basketball Association player and organization names.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
Potential overlap of box score tables between splits. This was extensively studied and pointed out by [1].
[1]: Thomson, Craig, Ehud Reiter, and Somayajulu Sripada. "SportSett: Basketball-A robust and maintainable data-set for Natural Language Generation." Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation. 2020.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Users may interact with a trained model to learn about a NBA game in a textual manner. On generated texts, they may observe factual errors that contradicts the actual data that the model conditions on.
Factual errors include wrong statistics of a player (e.g., 3PT), non-existent injury information.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Publishing the generated text as is. Even if the model achieves high scores on the evaluation metrics, there is a risk of factual errors mentioned above.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM/SciDuet | GEM | 2022-10-24T15:30:06Z | 300 | 1 | null | [
"task_categories:other",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"text-to-slide",
"region:us"
] | 2022-10-24T15:30:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- apache-2.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- other
task_ids: []
pretty_name: SciDuet
tags:
- text-to-slide
---
# Dataset Card for GEM/SciDuet
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/GEM/SciDuet
- **Repository:** https://github.com/IBM/document2slides/tree/main/SciDuet-ACL
- **Paper:** https://aclanthology.org/2021.naacl-main.111/
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/SciDuet).
### Dataset Summary
This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/SciDuet')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/SciDuet).
#### website
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### paper
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### authors
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/IBM/document2slides/tree/main/SciDuet-ACL)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.111",
doi = "10.18653/v1/2021.naacl-main.111",
pages = "1405--1418",
abstract = "Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years{'} NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.",
}
```
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Promote research on the task of document-to-slides generation
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Text-to-Slide
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
IBM Research
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
IBM Research
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Yufang Hou (IBM Research), Dakuo Wang (IBM Research)
### Dataset Structure
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. The text contents from multiple slides that correspond to the same slide title are mreged.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Training, validation and testing data contain 136, 55, and 81 papers from ACL Anthology and their corresponding slides, respectively.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The dataset integrated into GEM is the ACL portion of the whole dataset described in the [paper](https://aclanthology.org/2021.naacl-main.111), It contains the full Dev and Test sets, and a portion of the Train dataset.
Note that although we cannot release the whole training dataset due to copyright issues, researchers can still use our released data procurement code to generate the training dataset from the online ICML/NeurIPS anthologies.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, which requires a model has a good ability to "understand" long-form text, choose appropriate content and generate key points.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
content selection, long-form text undersanding and generation
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
content selection, long-form text undersanding and key points generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatical Evaluation Metric: ROUGE
Human Evaluation: (Readability, Informativeness, Consistency)
1) Readability: The generated slide content is coherent, concise, and grammatically correct;
2) Informativeness: The generated slide provides sufficient and necessary information that corresponds to the given slide title, regardless of its similarity to the original slide;
3) Consistency: The generated slide content is similar to the original author’s reference slide.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE + Human Evaluation
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Paper "D2S: Document-to-Slide Generation Via Query-Based
Text Summarization" reports 20.47, 5.26 and 19.08 for ROUGE-1, ROUGE-2 and ROUGE-L (f-score).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Provide a benchmark dataset for the document-to-slides task.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Text on papers was extracted through Grobid. Figures andcaptions were extracted through pdffigures. Text on slides was extracted through IBM Watson Discovery package and OCR by pytesseract. Figures and tables that appear on slides and papers were linked through multiscale template matching by OpenCV. Further dataset
cleaning was performed with standard string-based
heuristics on sentence building, equation and floating caption removal, and duplicate line deletion.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
the slide context text shouldn't contain additional format information such as "*** University"
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
The original dataset was open-sourced under Apache-2.0.
Some of the original dataset creators are part of the GEM v2 dataset infrastructure team and take care of integrating this dataset into GEM.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
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GEM/cs_restaurants | GEM | 2022-10-24T15:30:14Z | 300 | 1 | null | [
"task_categories:conversational",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:cs",
"license:cc-by-sa-4.0",
"dialog-response-generation",
"region:us"
] | 2022-10-24T15:30:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- cs
license:
- cc-by-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- conversational
task_ids: []
pretty_name: cs_restaurants
tags:
- dialog-response-generation
---
# Dataset Card for GEM/cs_restaurants
## Dataset Description
- **Homepage:** n/a
- **Repository:** https://github.com/UFAL-DSG/cs_restaurant_dataset
- **Paper:** https://aclanthology.org/W19-8670/
- **Leaderboard:** N/A
- **Point of Contact:** Ondrej Dusek
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/cs_restaurants).
### Dataset Summary
The Czech Restaurants dataset is a task oriented dialog dataset in which a model needs to verbalize a response that a service agent could provide which is specified through a series of dialog acts. The dataset originated as a translation of an English dataset to test the generation capabilities of an NLG system on a highly morphologically rich language like Czech.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/cs_restaurants')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/cs_restaurants).
#### website
n/a
#### paper
[Github](https://aclanthology.org/W19-8670/)
#### authors
Ondrej Dusek and Filip Jurcicek
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/UFAL-DSG/cs_restaurant_dataset)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[Github](https://aclanthology.org/W19-8670/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{cs_restaurants,
address = {Tokyo, Japan},
title = {Neural {Generation} for {Czech}: {Data} and {Baselines}},
shorttitle = {Neural {Generation} for {Czech}},
url = {https://www.aclweb.org/anthology/W19-8670/},
urldate = {2019-10-18},
booktitle = {Proceedings of the 12th {International} {Conference} on {Natural} {Language} {Generation} ({INLG} 2019)},
author = {Dušek, Ondřej and Jurčíček, Filip},
month = oct,
year = {2019},
pages = {563--574},
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ondrej Dusek
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
odusek@ufal.mff.cuni.cz
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
No breakdown of dialects is provided.
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Czech`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Six professional translators produced the outputs
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset was created to test neural NLG systems in Czech and their ability to deal with rich morphology.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Producing a text expressing the given intent/dialogue act and all and only the attributes specified in the input meaning representation.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Charles University, Prague
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ondrej Dusek and Filip Jurcicek
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
This research was supported by the Charles University project PRIMUS/19/SCI/10 and by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221. This work used using language resources distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2015071).
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Simon Mille wrote the initial data card and Yacine Jernite the data loader. Sebastian Gehrmann migrated the data card and loader to the v2 format.
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The data is stored in a JSON or CSV format, with identical contents. The data has 4 fields:
* `da`: the input meaning representation/dialogue act (MR)
* `delex_da`: the input MR, delexicalized -- all slot values are replaced with placeholders, such as `X-name`
* `text`: the corresponding target natural language text (reference)
* `delex_text`: the target text, delexicalized (delexicalization is applied regardless of inflection)
In addition, the data contains a JSON file with all possible inflected forms for all slot values in the dataset (`surface_forms.json`).
Each slot -> value entry contains a list of inflected forms for the given value, with the base form (lemma), the inflected form, and
a [morphological tag](https://ufal.mff.cuni.cz/pdt/Morphology_and_Tagging/Doc/hmptagqr.html).
The same MR is often repeated multiple times with different synonymous reference texts.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The data originated as a translation and localization of [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG dataset.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The input MRs were collected from [Wen et al.'s SF restaurant](https://www.aclweb.org/anthology/D15-1199/) NLG data
and localized by randomly replacing slot values (using a list of Prague restaurant names, neighborhoods etc.).
The generated slot values were then automatically replaced in reference texts in the data.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
"input": "inform_only_match(food=Turkish,name='Švejk Restaurant',near='Charles Bridge',price_range=cheap)",
"target": "Našla jsem pouze jednu levnou restauraci poblíž Karlova mostu , kde podávají tureckou kuchyni , Švejk Restaurant ."
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Property | Value |
|--------------------------------|-------|
| Total instances | 5,192 |
| Unique MRs | 2,417 |
| Unique delexicalized instances | 2,752 |
| Unique delexicalized MRs | 248 |
The data is split in a roughly 3:1:1 proportion into training, development and test sections, making sure no delexicalized MR
appears in two different parts. On the other hand, most DA types/intents are represented in all data parts.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The creators ensured that after delexicalization of the meaning representation there was no overlap between training and test.
The data is split at a 3:1:1 rate between training, validation, and test.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This is one of a few non-English data-to-text datasets, in a well-known domain, but covering a morphologically rich language that is harder to generate since named entities need to be inflected. This makes it harder to apply common techniques such as delexicalization or copy mechanisms.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The dialog acts in this dataset are much more varied than the e2e dataset which is the closest in style.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
surface realization
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
yes
#### Split Information
<!-- info: Describe how the new splits were created -->
<!-- scope: periscope -->
5 challenge sets for the Czech Restaurants dataset were added to the GEM evaluation suite.
1. Data shift: We created subsets of the training and development sets of 500 randomly selected inputs each.
2. Scrambling: We applied input scrambling on a subset of 500 randomly selected test instances; the order of the input dialogue acts was randomly reassigned.
3. We identified different subsets of the test set that we could compare to each other so that we would have a better understanding of the results. There are currently two selections that we have made:
The first comparison is based on input size: the number of predicates differs between different inputs, ranging from 1 to 5.
The table below provides an indication of the distribution of inputs with a particular length.
It is clear from the table that this distribution is not balanced, and comparisions between items should be done with caution.
Particularly for input size 4 and 5, there may not be enough data to draw reliable conclusions.
| Input length | Number of inputs |
|--------------|------------------|
| 1 | 183 |
| 2 | 267 |
| 3 | 297 |
| 4 | 86 |
| 5 | 9 |
The second comparison is based on the type of act. Again we caution against comparing the different groups that have relatively few items.
It is probably OK to compare `inform` and `?request`, but the other acts are all low-frequent.
| Act | Frequency |
|-------------------|-----------|
| ?request | 149 |
| inform | 609 |
| ?confirm | 22 |
| inform_only_match | 16 |
| inform_no_match | 34 |
| ?select | 12 |
#### Split Motivation
<!-- info: What aspects of the model's generation capacities were the splits created to test? -->
<!-- scope: periscope -->
Generalization and robustness.
### Getting Started with the Task
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- utterance: something a system or user may say in a turn
- meaning representation: a representation of meaning that the system should be in accordance with. The specific type of MR in this dataset are dialog acts which describe what a dialog system should do, e.g., inform a user about a value.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Surface realization
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `ROUGE`, `METEOR`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
This dataset uses the suite of word-overlap-based automatic metrics from the E2E NLG Challenge (BLEU, NIST, ROUGE-L, METEOR, and CIDEr). In addition, the slot error rate is measured.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset was created to test neural NLG systems in Czech and their ability to deal with rich morphology.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text expressing the given intent/dialogue act and all and only the attributes specified in the input MR.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Created for the dataset`
#### Creation Process
<!-- info: If created for the dataset, describe the creation process. -->
<!-- scope: microscope -->
Six professional translators translated the underlying dataset with the following instructions:
- Each utterance should be translated by itself
- fluent spoken-style Czech should be produced
- Facts should be preserved
- If possible, synonyms should be varied to create diverse utterances
- Entity names should be inflected as necessary
- the reader of the generated text should be addressed using formal form and self-references should use the female form.
The translators did not have access to the meaning representation.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
It was not explicitly stated but we can safely assume that the translators agreed to this use of their data.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
This dataset does not include any information about individuals.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
The dataset may help improve NLG methods for morphologically rich languages beyond Czech.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
yes
#### Links and Summaries of Analysis Work
<!-- info: Provide links to and summaries of works analyzing these biases. -->
<!-- scope: microscope -->
To ensure consistency of translation, the data always uses formal/polite address for the user, and uses the female form for first-person self-references (as if the dialogue agent producing the sentences was female). This prevents data sparsity and ensures consistent results for systems trained on the dataset, but does not represent all potential situations arising in Czech.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
The test set may lead users to over-estimate the performance of their NLG systems with respect to their generalisability, because there are no unseen restaurants or addresses in the test set. This is something we will look into for future editions of the GEM shared task.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1646052073 | GEM-submissions | 2022-02-28T12:41:18Z | 300 | 0 | null | [
"benchmark:gem",
"evaluation",
"benchmark",
"region:us"
] | 2022-02-28T12:41:18Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
tags:
- evaluation
- benchmark
---
# GEM Submission
Submission name: Hugging Face test T5-base.outputs.json 36bf2a59
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JonathanSum/sv_corpora_parliament_processed | JonathanSum | 2022-02-17T06:48:11Z | 300 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Langame/waiting-messages | Langame | 2021-12-19T08:56:56Z | 300 | 0 | null | [
"region:us"
] | 2021-12-19T08:56:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Langame/waiting-messages
Generated using OpenAI GPT-3 davinci-codex based on random initial samples written by a human.
⚠️ The dataset has not been de-duplicated, so there may be duplicates. ⚠️ | [
-0.37135204672813416,
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