Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering (2020.emnlp-main)
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| Challenge: | despite rapid progress in multihop question-answering, models still have trouble explaining why an answer is correct. |
| Approach: | They propose three explanation datasets in which explanations from corpus facts are annotated . they first annotate multiple candidate explanations for each answer, then use crowd-sourcing perturbations to test generalization . |
| Outcome: | The proposed datasets improve explanation quality but still behind the upper bound . the proposed dataset can be used to improve explanations using a BERT-based classifier . |
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Matthew Lamm, Jennimaria Palomaki, Chris Alberti, Daniel Andor, Eunsol Choi, Livio Baldini Soares, Michael Collins
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| Challenge: | Existing models have outperformed humans on question answering datasets, but they have yet to outperform humans on the task of question answering itself. |
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Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, Christopher D. Manning
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Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, Peter Clark
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| Challenge: | a tutorial aims to provide an up-to-date guide to the recent datasets . the target audience is the NLP practitioners who are lost in dozens of the recent data sets. |
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Explanations for CommonsenseQA: New Dataset and Models (2021.acl-long)
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Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, Dinesh Garg
| Challenge: | a dataset called CommonsenseQA (CQA) was recently released to advance the research on common-sense question answering (QA) |
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Learning to Explain: Generating Stable Explanations Fast (2021.acl-long)
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| Challenge: | Existing methods for explaining outcome of machine learning models produce explanations, or rationales, which identify the attributions of features in an input example. |
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