| Challenge: | Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks. |
| Approach: | They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem. |
| Outcome: | Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models. |
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| Challenge: | Recent research has explored how to improve the abilities of decision-making and question generation. |
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ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension (2022.coling-1)
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| Challenge: | Existing methods require three steps to understand text, but span extraction and question rephrasing steps are not fully exploited. |
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| Challenge: | Existing studies train independent or pipeline systems for the two subtasks but are trivial by using hard-label decisions to activate question generation. |
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Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension (2023.acl-long)
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| Challenge: | Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life conversation scenes. |
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Data Collection and End-to-End Learning for Conversational AI (D19-2)
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| Challenge: | tutorial aims to familiarise research community with recent advances in statistical dialogue systems . focus of tutorial is on learning end-to-end from data and their relation to more common modular systems. |
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Proceedings of the 2nd Workshop on Machine Reading for Question Answering (D19-58)
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End-to-end Spoken Conversational Question Answering: Task, Dataset and Model (2022.findings-naacl)
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| Challenge: | Existing methods for conversational question answering significantly degrade on datasets . a new task aims to enable systems to model complex dialogues flow given the speech documents . |
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