Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension
Xiao Zhang, Heyan Huang, Zewen Chi, Xian-Ling Mao · 2023
Open-retrieval conversational machine reading comprehension (OCMRC) simulates reallife conversational interaction scenes.Machines are required to make a decision of Yes/No/Inquire or generate a follow-up question when the decision is Inquire based on retrieved rule texts, user scenario, user question and dialogue history.Recent studies try to reduce the information gap between decision-making and question generation, in order to improve the performance of generation.However, the information gap still persists because these methods are still limited in pipeline framework, where decision-making and question generation are performed separately, making it hard to share the entailment reasoning used in decision-making across all stages.To tackle the above problem, we propose a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decisionmaking and question generation in a global understanding manner.The extensive experimental results demonstrate that our proposed framework achieves new state-of-the-art performance on the OR-ShARC benchmark.Our model and code are publicly available 1 .