Open-retrieval conversational machine reading comprehension (OCMRC) simulates real-life 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 explored the methods to reduce the information gap between decision-making and question generation and thus improve the performance of generation. However, the information gap still exists because these pipeline structures are still limited in decision-making, span extraction, and question rephrasing three stages. Decision-making and generation are reasoning separately, and the entailment reasoning utilized in decision-making is hard to share through all stages. To tackle the above problem, we proposed a novel one-stage end-to-end framework, called Entailment Fused-T5 (EFT), to bridge the information gap between decision-making and 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.
翻译:开放检索对话机阅读理解(OCMRC)模拟真实的谈话互动场景,要求机器在根据检索的规则文本、用户情景、用户问题和对话历史作出“询问”决定时,作出“是/否/问”决定或提出后续问题。最近的研究探讨了缩小决策和问题生成之间的信息差距的方法,从而改进了生成的绩效。然而,信息差距仍然存在,因为这些管道结构在决策、抽取和问题重新划分三个阶段方面仍然有限。决策和生成是分开进行推理的,决策中使用的必然推理在所有阶段都是难以分享的。为了解决上述问题,我们提出了一个名为Entailation Fuse-T5(EFT)的新阶段端到端框架,以全球理解的方式弥合决策与生成之间的信息差距。广泛的实验结果表明,我们提议的框架在OR-SARC基准上实现了新的状态表现。