论文标题
ET5：一个新颖的端到端对话机器阅读理解的框架
ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension
论文作者
论文摘要
对话机器阅读理解（CMRC）旨在帮助计算机理解自然语言文本，然后进行多转交谈以回答与文本有关的问题。现有方法通常需要三个步骤：（1）基于需要推理的决策； （2）如果上述决定的要求，请跨越提取； （3）基于提取的跨度重新启动问题。但是，对于几乎所有这些方法，跨度提取和问题的改写步骤无法完全利用决策制定步骤中的精细元素支出推理信息，因为它们的相对独立性将进一步扩大决策制定和问题措辞之间的信息差距。因此，为了解决这个问题，我们提出了一个基于共享参数机制的对话机读取理解理解的新型端到端框架，称为Intailment推理T5（ET5）。尽管我们提出的框架轻巧，但实验结果表明，拟议的ET5以55.2的BLEU-4分数在Sharc排行榜上取得了新的最新结果。我们的模型和代码可在https://github.com/yottaxx/et5上公开获取。
Conversational machine reading comprehension (CMRC) aims to assist computers to understand an natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. Existing methods typically require three steps: (1) decision making based on entailment reasoning; (2) span extraction if required by the above decision; (3) question rephrasing based on the extracted span. However, for nearly all these methods, the span extraction and question rephrasing steps cannot fully exploit the fine-grained entailment reasoning information in decision making step because of their relative independence, which will further enlarge the information gap between decision making and question phrasing. Thus, to tackle this problem, we propose a novel end-to-end framework for conversational machine reading comprehension based on shared parameter mechanism, called entailment reasoning T5 (ET5). Despite the lightweight of our proposed framework, experimental results show that the proposed ET5 achieves new state-of-the-art results on the ShARC leaderboard with the BLEU-4 score of 55.2. Our model and code are publicly available at https://github.com/Yottaxx/ET5.
