| Challenge: | a dataset of 2,437 dialogues and 10,917 QA pairs is used to access domain-specific FAQ information. |
| Approach: | They present a dataset with 2,437 dialogues and 10,917 QA pairs for FAQs . they use the Wizard of Oz method with crowdsourcing to create dialogues using the original post and the original reply. |
| Outcome: | The proposed system can access domain-specific FAQ information without training data. |
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| Challenge: | Existing open-domain QA tasks focus on questions whose answer can be deduced directly from global factual knowledge. |
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| Challenge: | tutorial provides a comprehensive overview of cutting-edge research in open-domain question answering (QA) |
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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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PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them (2021.tacl-1)
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Patrick Lewis, Yuxiang Wu, Linqing Liu, Pasquale Minervini, Heinrich Küttler, Aleksandra Piktus, Pontus Stenetorp, Sebastian Riedel
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Open-Domain Question Answering Goes Conversational via Question Rewriting (2021.naacl-main)
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Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
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MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering (2021.tacl-1)
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| Challenge: | Existing multilingual QA datasets lack linguistic diversity and comparable evaluation between languages. |
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