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Open-retrieval conversational machine reading comprehension (OCMRC) simulatesreal-life conversational interaction scenes. Machines are required to make adecision of "Yes/No/Inquire" or generate a follow-up question when the decisionis "Inquire" based on retrieved rule texts, user scenario, user question, anddialogue history. Recent studies explored the methods to reduce the informationgap between decision-making and question generation and thus improve theperformance of generation. However, the information gap still exists becausethese pipeline structures are still limited in decision-making, spanextraction, and question rephrasing three stages. Decision-making andgeneration are reasoning separately, and the entailment reasoning utilized indecision-making is hard to share through all stages. To tackle the aboveproblem, we proposed a novel one-stage end-to-end framework, called EntailmentFused-T5 (EFT), to bridge the information gap between decision-making andgeneration in a global understanding manner. The extensive experimental resultsdemonstrate that our proposed framework achieves new state-of-the-artperformance on the OR-ShARC benchmark.

Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension