Comprehending a dialogue requires a model to capture diverse kinds of key
information in the utterances, which are either scattered around or implicitly
implied in different turns of conversations. Therefore, dialogue comprehension
requires diverse capabilities such as paraphrasing, summarizing, and
commonsense reasoning. Towards the objective of pre-training a zero-shot
dialogue comprehension model, we develop a novel narrative-guided pre-training
strategy that learns by narrating the key information from a dialogue input.
However, the dialogue-narrative parallel corpus for such a pre-training
strategy is currently unavailable. For this reason, we first construct a
dialogue-narrative parallel corpus by automatically aligning movie subtitles
and their synopses. We then pre-train a BART model on the data and evaluate its
performance on four dialogue-based tasks that require comprehension.
Experimental results show that our model not only achieves superior zero-shot
performance but also exhibits stronger fine-grained dialogue comprehension
capabilities. The data and code are available at
this https URL

本文提出了一种新的叙述引导的预训练策略，以学习从对话输入中叙述关键信息的能力，针对当前方法缺失的对话 - 叙述并行语料库，本文利用自动对齐的电影字幕和简介构建了一个这样的语料库，在此基础上，用一个 BART 模型进行预训练，并在四个需要理解的基于对话的任务上评估其性能。实验结果表明，本文所提出的方法不仅在零 - shot 性能上表现出了优越性，而且展示了更强的细粒度的对话理解能力。