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license: mit
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---
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license: mit
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---
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# Dataset Card for FeedbackQA
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage: https://mcgill-nlp.github.io/feedbackQA/**
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- **Repository: https://github.com/McGill-NLP/feedbackQA**
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- **Paper:**
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- **Leaderboard:**
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### Dataset Summary
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FeedbackQA is a retrieval-based QA dataset that contains interactive feedback from users.
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It has two parts: the first part contains a conventional RQA dataset,
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whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs.
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Please find the full dataset [here](https://github.com/McGill-NLP/feedbackQA_data).
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### Languages
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English
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## Dataset Creation
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For each question-answer pair, we collected multiple feedback, each of which consists of a rating, selected
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from excellent, good, could be improved, bad, and a natural language explanation
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elaborating on the strengths and/or weaknesses of the answer.
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#### Initial Data Collection and Normalization
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We scraped Covid-19-related content from official websites.
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### Annotations
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#### Who are the annotators?
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Crowd-workers
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### Licensing Information
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[More Information Needed]
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### Contributions
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[McGill-NLP](https://github.com/McGill-NLP)
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