论文标题
按纳尔语学习：零击对话理解的叙事预培训
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
论文作者
论文摘要
理解对话需要一个模型来捕获话语中的各种关键信息，这些信息散布在不同的对话转弯中。因此，对话理解需要各种能力，例如释义，总结和常识推理。为了预先培训零摄像的对话理解模型，我们制定了一种新颖的叙事引导的预训练策略，该策略通过从对话输入中叙述关键信息来学习。但是，目前无法使用这种预训练策略的对话叙事平行语料库。因此，我们首先通过自动使电影字幕及其概述来构建对话叙事平行语料库。然后，我们在数据上预先培训BART模型，并在需要理解的四个基于对话的任务上评估其性能。实验结果表明，我们的模型不仅达到了较高的零击性能，而且还具有更强的细粒对话理解能力。数据和代码可从https://github.com/zwaochaocs/diana获得
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 https://github.com/zhaochaocs/Diana
