FlowDelta: Modeling Flow Information Gain in Reasoning for Conversational Machine Comprehension (D19-58)
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| Challenge: | Existing machine comprehension models focus on a single-turn setting and do not account for previous reasoning processes. |
| Approach: | They propose to explicitly model the information gain through the dialogue reasoning . they propose to apply the proposed mechanism to other machine comprehension models . |
| Outcome: | The proposed model achieves state-of-the-art performance in a conversational QA dataset QuAC and a sequential instruction understanding dataset SCONE. |
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| Challenge: | Existing knowledge grounding models focus on locating knowledge in document contexts that are relevant to the conversation. |
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| Challenge: | Conversational machine comprehension (CMC) is a research track in conversational AI. |
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| Challenge: | Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation. |
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| Challenge: | Current datasets for conversational question answering lack realistic, domain-specific training data. |
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QuAC: Question Answering in Context (D18-1)
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Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, Luke Zettlemoyer
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Prediction or Comparison: Toward Interpretable Qualitative Reasoning (2021.findings-acl)
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| Challenge: | Qualitative relationships are a significant portion of textual knowledge . current approaches use semantic parsers to transform natural language inputs into logical expressions or a "black-box" model to solve them in one step. |
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Flowchart-Based Decision Making with Large Language Models (2025.findings-acl)
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| Challenge: | Large language models face significant challenges in interpretability of dialogue flow and reproducibility of expert knowledge. |
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