Datasets:
Tasks:
Text Classification
Sub-tasks:
text-scoring
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
| task_categories: | |
| - text-classification | |
| multilinguality: | |
| - monolingual | |
| task_ids: | |
| - text-scoring | |
| language: | |
| - en | |
| annotations_creators: | |
| - crowdsourced | |
| source_datasets: | |
| - extended | |
| size_categories: | |
| - 10K<n<100K | |
| license: | |
| - cc-by-sa-4.0 | |
| paperswithcode_id: null | |
| pretty_name: GoogleWellformedQuery | |
| language_creators: | |
| - found | |
| dataset_info: | |
| features: | |
| - name: rating | |
| dtype: float32 | |
| - name: content | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 857391 | |
| num_examples: 17500 | |
| - name: test | |
| num_bytes: 189503 | |
| num_examples: 3850 | |
| - name: validation | |
| num_bytes: 184110 | |
| num_examples: 3750 | |
| download_size: 1157019 | |
| dataset_size: 1231004 | |
| # Dataset Card for Google Query-wellformedness Dataset | |
| ## 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:** [GitHub](https://github.com/google-research-datasets/query-wellformedness) | |
| - **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness) | |
| - **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419) | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### Dataset Summary | |
| Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. | |
| ### Supported Tasks and Leaderboards | |
| [More Information Needed] | |
| ### Languages | |
| English | |
| ## Dataset Structure | |
| ### Data Instances | |
| ``` | |
| {'rating': 0.2, 'content': 'The European Union includes how many ?'} | |
| ``` | |
| ### Data Fields | |
| - `rating`: a `float` between 0-1 | |
| - `sentence`: query which you want to rate | |
| ### Data Splits | |
| | | Train | Valid | Test | | |
| | ----- | ------ | ----- | ---- | | |
| | Input Sentences | 17500 | 3750 | 3850 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. | |
| ### Source Data | |
| Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”). | |
| #### Initial Data Collection and Normalization | |
| Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases. | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| The queries are annotated into well-formed or non-wellformed questions if it satisfies the following: | |
| 1. Query is grammatical. | |
| 2. Query is an explicit question. | |
| 3. Query does not contain spelling errors. | |
| #### Who are the annotators? | |
| Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed. | |
| ### 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 | |
| Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied. | |
| ### Citation Information | |
| ``` | |
| @InProceedings{FaruquiDas2018, | |
| title = {{Identifying Well-formed Natural Language Questions}}, | |
| author = {Faruqui, Manaal and Das, Dipanjan}, | |
| booktitle = {Proc. of EMNLP}, | |
| year = {2018} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. |