TopiOCQA: Open-domain Conversational Question Answering with Topic Switching (2022.tacl-1)
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| Challenge: | Current datasets for conversational question answering do not contain topic switches . people often engage in information-seeking conversations to discover new knowledge . |
| Approach: | They propose an open-domain conversational dataset with topic switches based on Wikipedia. |
| Outcome: | The proposed dataset achieves an F1 of 55.8, falling short of human performance by 14.2 points, indicating the difficulty of the dataset. |
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Raviteja Anantha, Svitlana Vakulenko, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
| Challenge: | Existing large-scale benchmarks for conversational QA limit the topic of conversation to the content of a single document. |
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Talk to Papers: Bringing Neural Question Answering to Academic Search (2020.acl-demos)
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| Challenge: | Talk to Papers aims to improve the current experience of academic search by using open-domain question answering (QA) techniques. |
| Approach: | They propose to use open-domain question answering techniques to improve the current experience of academic search by combining natural language queries with machine reading at scale. |
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| Challenge: | Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process. |
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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 . |
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OTTers: One-turn Topic Transitions for Open-Domain Dialogue (2021.acl-long)
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| Challenge: | a mixed-initiative dialogue system is often purely responsive, make abrupt transitions, or fail to take initiative. |
| Approach: | They propose a task to generate a "bridging" utterance connecting a new topic to the previous conversation turn. |
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DoQA - Accessing Domain-Specific FAQs via Conversational QA (2020.acl-main)
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| 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. |
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Open-Domain Conversational Question Answering with Historical Answers (2022.findings-aacl)
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| Challenge: | Existing approaches to conversational question answering are limited due to the large number of candidate documents. |
| Approach: | They propose a model that leverages historical answers to boost retrieval performance . they propose to use open-domain conversational question answering to solve these problems . |
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Phrase Retrieval for Open Domain Conversational Question Answering with Conversational Dependency Modeling via Contrastive Learning (2023.findings-acl)
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| Challenge: | Open-Domain Conversational Question Answering (ODConvQA) aims to answer questions through a multi-turn conversation . however, such a pipeline approach makes the reader vulnerable to errors propagated from the retriever, which makes it slower since they are not runnable in parallel. |
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XQA: A Cross-lingual Open-domain Question Answering Dataset (P19-1)
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| Challenge: | Open-domain question answering aims to answer questions through text retrieval and reading comprehension . but, the success of these models relies on a massive volume of training data, which is not available in other languages . a new dataset aims at investigating cross-lingual OpenQA . |
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MMCoQA: Conversational Question Answering over Text, Tables, and Images (2022.acl-long)
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| Challenge: | Existing conversational QA systems only use a single knowledge source, e.g., paragraphs or knowledge graph, and assume it contains enough evidence to extract answers to users' questions. |
| Approach: | They propose a task to answer users' questions with multimodal knowledge sources via multi-turn conversations using a multimodal dataset. |
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