Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
| Challenge: | a dataset for Question Answering in Context contains 14K information-seeking QA dialogs . questions are often more open-ended, unanswerable, or only meaningful within the dialog context . |
| Approach: | They propose a dataset for Question Answering in Context that contains 14K dialogs . they use a student to ask questions about a Wikipedia section and a teacher to answer them . |
| Outcome: | The proposed dataset underperforms humans in a number of reference models . the dataset contains 14K information-seeking dialogs over sections from Wikipedia . |
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| Challenge: | Existing QA datasets lack key NLP problems like coreference and ellipsis resolution. |
| Approach: | They propose a task of question-in-context rewriting to rewrite a context-dependent question into a self-contained question with the same answer. |
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Discourse Comprehension: A Question Answering Framework to Represent Sentence Connections (2022.emnlp-main)
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| Challenge: | Existing systems for text comprehension are inadequate for more holistic comprehension of a discourse. |
| Approach: | They propose a new paradigm that captures both discourse and semantic links between sentences in the form of free-form, open-ended questions. |
| Outcome: | The proposed model captures discourse and semantic links between sentences in the form of free-form, open-ended questions. |
ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning (2020.emnlp-main)
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| Challenge: | Existing question answering datasets for common sense reasoning are lacking for prototypical situations. |
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A dataset and baselines for sequential open-domain question answering (D18-1)
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| Challenge: | Existing question-answering systems focus on answering individual questions, assuming they are devoid of context. |
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PragmatiCQA: A Dataset for Pragmatic Question Answering in Conversations (2023.findings-acl)
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| Challenge: | Mars? - PragmatiCQA |
| Approach: | Mars? - The Paper . |
| Outcome: | The proposed dataset features 6873 QA pairs that explores pragmatic reasoning in conversations over a diverse set of topics. |
A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC (N19-1)
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| Challenge: | In response to this development, there have been a flurry of new datasets for question answering. |
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FocusQA: Open-Domain Question Answering with a Context in Focus (2022.findings-emnlp)
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Gianni Barlacchi, Ivano Lauriola, Alessandro Moschitti, Marco Del Tredici, Xiaoyu Shen, Thuy Vu, Bill Byrne, Adrià de Gispert
| Challenge: | a new method for question answering with a context in focus simulates a free interaction with QA systems. |
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NoiseQA: Challenge Set Evaluation for User-Centric Question Answering (2021.eacl-main)
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| Challenge: | Question-Answering (QA) systems are deployed in the real world . a lack of research attention has been devoted to studying the issues that arise when people use QA systems. |
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What do Models Learn from Question Answering Datasets? (2020.emnlp-main)
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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. |
| Approach: | They evaluate BERT-based question answering models on their generalizability to out-of-domain examples, responses to missing or incorrect data, and ability to handle question variations. |
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ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters (N19-1)
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| Challenge: | ComQA dataset captures question phenomena and the diverse ways in which they are formulated. |
| Approach: | They propose a large dataset of real user questions that captures question phenomena and the diverse ways in which they are formulated. |
| Outcome: | The proposed dataset can be a driver of future research on factoid question answering (QA). |