|
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| pretty_name: WikiQA Evaluation Dataset |
| tags: |
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| * wikiqa |
| * information-retrieval |
| * retrieval |
| * evaluation |
| * question-answering |
| * benchmark |
| |
| --- |
| |
| # WikiQA Evaluation Dataset |
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| This dataset is a processed evaluation dataset derived from the **WikiQA development split**. |
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| The dataset was constructed for evaluating query-document relevance in information retrieval and retrieval evaluation experiments. |
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| ## Dataset Description |
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| The original **WikiQA** dataset provides questions and candidate answer sentences with human-annotated relevance labels. |
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| For this dataset, the WikiQA development split (`WikiQA-dev.tsv`) was used as the source data. Positive question-sentence pairs were extracted from the original annotations, and additional negative examples were randomly sampled to construct an evaluation dataset with a specified positive-to-negative ratio. |
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| No model-based hard-negative mining was performed when constructing this evaluation dataset. |
| |
| ## Source Dataset |
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| This dataset is derived from the **WikiQA** dataset introduced by: |
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| > Yang, Yi, Wen-tau Yih, and Christopher Meek. |
| > "WikiQA: A Challenge Dataset for Open-Domain Question Answering." |
| > Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015. |
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| The original WikiQA corpus was released by **Microsoft Research**. |
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| The source files used for preprocessing were obtained from the following Kaggle repository: |
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| https://www.kaggle.com/datasets/saurabhshahane/wikiqa-corpus |
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| The Kaggle dataset is used here as the source from which the WikiQA development split was obtained; the original dataset should be attributed to Microsoft Research and the original WikiQA authors. |
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| ## Dataset Construction |
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| The dataset was constructed from `WikiQA-dev.tsv` using the following procedure: |
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| 1. The WikiQA development split was loaded as the source dataset. |
| 2. Question-sentence pairs labeled as relevant in the original WikiQA annotations were extracted as positive examples. |
| 3. Non-relevant question-sentence pairs were randomly sampled from the available negative examples. |
| 4. The number of negative examples was controlled to obtain the desired positive-to-negative ratio. |
| 5. The resulting examples were used as the evaluation dataset. |
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| The purpose of this processing was to create a compact evaluation set while preserving the relevance annotations provided by WikiQA. |
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| ### Negative Sampling |
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| Negative examples in this dataset are **randomly sampled negatives**. |
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| ## Dataset Structure |
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| Each example contains the following fields: |
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| | Field | Description | |
| | ---------- | --------------------------- | |
| | `Question` | The question from WikiQA | |
| | `Sentence` | A candidate answer sentence | |
| | `label` | Binary relevance label | |
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| Where: |
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| * `1` indicates that the sentence is annotated as a relevant answer to the question. |
| * `0` indicates that the sentence is not annotated as a relevant answer. |
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| > Update the field names above if the actual Hugging Face dataset uses different column names. |
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| ## Intended Use |
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| This dataset can be used for: |
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| * evaluating information retrieval models, |
| * evaluating query-document relevance models, |
| * evaluating retrieval evaluators, |
| * comparing retrieval or reranking methods, |
| * experiments involving binary relevance classification. |
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| Although this dataset was originally created for retrieval-evaluation experiments, it is not restricted to a particular model or retrieval framework. |
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| ## Relationship to the Original WikiQA Dataset |
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| This repository contains a **processed subset/reconstruction of the WikiQA development split**, rather than the original WikiQA corpus. |
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| The original question, sentence, and relevance annotations originate from WikiQA. The positive-example extraction and negative-example sampling used to construct this evaluation dataset were performed separately for this repository. |
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| This dataset should therefore not be considered an official release of the WikiQA benchmark. |
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| ## License |
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| This dataset is derived from the WikiQA dataset distributed by Microsoft Research under the **Open Use of Data Agreement v1.0 (O-UDA-1.0)**. |
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| The original license permits use, modification, and distribution of the data subject to its terms. |
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| Users of this dataset should review the original license and retain the required attribution and license information. |
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| Original license: |
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| **Open Use of Data Agreement v1.0 (O-UDA-1.0)** |
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| License identifier: `O-UDA-1.0` |
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| ## Attribution |
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| Please attribute the original WikiQA dataset to Microsoft Research and the original authors: |
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| Yang, Yi, Wen-tau Yih, and Christopher Meek. |
| "WikiQA: A Challenge Dataset for Open-Domain Question Answering." |
| Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2015. |
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| ## Citation |
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| If you use this dataset, please cite the original WikiQA paper: |
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| ```bibtex |
| @inproceedings{yang2015wikiqa, |
| title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering}, |
| author = {Yang, Yi and Yih, Wen-tau and Meek, Christopher}, |
| booktitle = {Proceedings of the 2015 Conference on Empirical Methods |
| in Natural Language Processing}, |
| year = {2015} |
| } |
| ``` |
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| ## Acknowledgements |
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| This dataset is based on the WikiQA corpus released by Microsoft Research. |
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| The author of this repository is responsible only for the preprocessing and sampling procedure described above and does not claim ownership of the original WikiQA data. |
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