A few-shot neuro-symbolic pipeline decomposes policies into logic formulas and evaluates them with three-valued logic, reaching near state-of-the-art accuracy on ShARC without task-specific fine-tuning of its decomposition modules.
Dialogue Graph Modeling for Conversational Machine Reading
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abstract
Conversational Machine Reading (CMR) aims at answering questions in a complicated manner. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and ask questions to clarify if necessary. In this paper, we propose a dialogue graph modeling framework to improve the understanding and reasoning ability of machine on CMR task. There are three types of graph in total. Specifically, Discourse Graph is designed to learn explicitly and extract the discourse relation among rule texts as well as the extra knowledge of scenario; Decoupling Graph is used for understanding local and contextualized connection within rule texts. And finally a global graph for fusing the information together and reply to the user with our final decision being either "Yes/No/Irrelevant" or to ask a follow-up question to clarify.
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cs.CL 1years
2025 1verdicts
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Few-shot Policy (de)composition in Conversational Question Answering
A few-shot neuro-symbolic pipeline decomposes policies into logic formulas and evaluates them with three-valued logic, reaching near state-of-the-art accuracy on ShARC without task-specific fine-tuning of its decomposition modules.