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--- |
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viewer: false |
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language: |
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- ace |
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- amh |
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- ara |
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- arq |
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- ary |
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- bam |
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- ban |
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- bbc |
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- ben |
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- bjn |
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- bos |
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- bug |
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- bul |
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- ces |
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- dan |
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- deu |
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- ell |
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- eng |
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- fas |
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- fil |
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- fin |
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- fre |
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- hau |
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- heb |
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- hin |
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- hrv |
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- hun |
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- ibo |
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- ind |
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- ita |
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- jav |
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- jpn |
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- kan |
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- kin |
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- kor |
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- mad |
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- mal |
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- mar |
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- min |
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- mlt |
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- nij |
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- nor |
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- pcm |
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- pol |
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- por |
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- ron |
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- rus |
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- slk |
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- slv |
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- spa |
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- sqi |
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- srp |
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- sun |
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- swe |
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- swh |
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- tam |
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- tel |
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- tha |
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- tso |
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- tur |
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- twi |
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- vie |
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- yor |
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- zho |
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tags: |
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- Anti-Social |
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- Emotion Recognition |
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- Humor Detection |
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- Irony |
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- Sarcasm |
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- Sentiment Analysis |
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- Subjectivity Analysis |
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- hate speech detection |
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- offensive language detection |
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task_categories: |
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- text-classification |
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extra_gated_fields: |
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Full Name: text |
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Official Email Address: text |
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Affiliation: text |
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Country: text |
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I agree to ONLY use this dataset for non-commercial purposes: checkbox |
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I agree to cite the SPARROW paper and all original papers: checkbox |
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--- |
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<p align="center"> |
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<br> |
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<img src="https://sparrow.dlnlp.ai/img/sparrow_main2.jpg" width="70%"/> |
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<br> |
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<p> |
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<p align="center"> |
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<!-- <a href="https://github.com/UBC-NLP/sparraw/releases"> --> |
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<!-- <img alt="GitHub release" src="https://img.shields.io/github/release/UBC-NLP/sparraw.svg"> </a>--> |
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<a href="https://sparrow.dlnlp.ai/"> |
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<img alt="Documentation" src="https://img.shields.io/website.svg?down_color=red&down_message=offline&up_message=online&url=https://sparrow.dlnlp.ai"> |
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</a> |
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</p> |
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In this work, we introduce [**SPARROW**](https://arxiv.org/abs/2310.14557), SPARROW is a evaluation benchmark for sociopragmatic meaning understanding. SPARROW comprises 169 datasets covering 13 task types across six primary categories (e.g., anti-social language detection, emotion recognition). SPARROW datasets encompass 64 different languages originating from 12 language families representing 16 writing scripts. |
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# How to Use SPARROW |
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### Request Access ### |
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To obtain access to the SPARROW benchmark on Huggingface, follow the following steps: |
|
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- Login on your Haggingface account |
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|
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<img src="https://sparrow.dlnlp.ai/img/hf_login_request.png" width="70%"/> |
|
|
- Request access |
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|
* Please fill in your actual full name and affiliation (e.g., the name of your research institute). |
|
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* Please use your official email address if it is available. |
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<img src="https://sparrow.dlnlp.ai/img/sparrow_request.png" width="70%"/> |
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## Install Requirments |
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```shell |
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pip install datasets transformers seqeval |
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|
``` |
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|
### Login with your Huggingface CLI ### |
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|
You can get/manage your access tokens in your [settings](https://huggingface.co/docs/hub/security-tokens). |
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```shell |
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export HUGGINGFACE_TOKEN="" |
|
|
huggingface-cli login --token $HUGGINGFACE_TOKEN |
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|
``` |
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## Submitting your results on SPARROW test |
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We design a public leaderboard for scoring PLMs on SPARRAW. Our leaderboard is interactive and offers rich meta-data about the various datasets involved as well as the language models we evaluate. |
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You can evalute your models using **SPARROW** leaderboard: **[https://sparrow.dlnlp.ai](https://sparrow.dlnlp.ai)** |
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If you want to get the labels of Test set, please contact Chiyu via (zcy94@outlook.com) |
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--- |
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## Citation |
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If you use SPARROW for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows: |
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|
```bibtex |
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|
@inproceedings{zhang-etal-2023-skipped, |
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title = "The Skipped Beat: A Study of Sociopragmatic Understanding in LLMs for 64 Languages", |
|
|
author = "Zhang, Chiyu and |
|
|
Khai Duy Doan and, |
|
|
Qisheng Liao and, |
|
|
Abdul-Mageed, Muhammad", |
|
|
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
|
|
year = "2023", |
|
|
publisher = "Association for Computational Linguistics", |
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|
} |
|
|
``` |
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--- |
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## Acknowledgments |
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|
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). |
