论文标题
Strudel：对话理解的结构化对话摘要
STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension
论文作者
论文摘要
抽象性对话摘要长期以来一直被视为自然语言处理中的一项重要独立任务，但是以前没有工作探讨了是否可以将抽象性对话摘要摘要还可以用作提高NLP系统在其他重要对话理解任务上的表现的手段。在本文中，我们提出了一种新型的对话摘要任务 - 结构化对话摘要 - 可以帮助预训练的语言模型更好地理解对话并提高其在重要对话理解任务上的表现。我们进一步收集了超过400个对话的Strudel摘要的人类注释，并引入了新的Strudel对话理解建模框架，该框架将Strudel集成到基于图形的网络网络网络网络的对话推理上，而不是变形金刚编码器语言模型，以提高他们的对话理解能力。在我们对两个重要下游对话理解任务的经验实验中 - 对话问题回答和对话回答预测 - 我们表明，我们的Strudel对话理解模型可以显着提高变形金刚编码器语言模型的对话理解性能。
Abstractive dialogue summarization has long been viewed as an important standalone task in natural language processing, but no previous work has explored the possibility of whether abstractive dialogue summarization can also be used as a means to boost an NLP system's performance on other important dialogue comprehension tasks. In this paper, we propose a novel type of dialogue summarization task - STRUctured DiaLoguE Summarization - that can help pre-trained language models to better understand dialogues and improve their performance on important dialogue comprehension tasks. We further collect human annotations of STRUDEL summaries over 400 dialogues and introduce a new STRUDEL dialogue comprehension modeling framework that integrates STRUDEL into a graph-neural-network-based dialogue reasoning module over transformer encoder language models to improve their dialogue comprehension abilities. In our empirical experiments on two important downstream dialogue comprehension tasks - dialogue question answering and dialogue response prediction - we show that our STRUDEL dialogue comprehension model can significantly improve the dialogue comprehension performance of transformer encoder language models.
