论文标题
话语级对话理解：一项实证研究
Utterance-level Dialogue Understanding: An Empirical Study
论文作者
论文摘要
网络和其他地方的最近大量的对话数据要求有效的NLP系统来理解对话。完整的话语水平的理解通常需要在附近的话语中定义的上下文理解。近年来，已经提出了许多关于理解任务的话语级对话的方法。这些方法中的大多数是有效理解的上下文。在本文中，我们使用最先进的对话框将方法理解为基准，探索和量化了对话，即情感，意图和对话行为的不同方面的作用。具体而言，我们采用各种扰动来扭曲给定话语的背景，并研究其对不同任务和基准的影响。这为我们提供了对对话不同方面的基本背景控制因素的见解。这样的见解可以激发更有效的对话理解模型，并为未来的文本生成方法提供支持。与这项工作有关的实施可在https://github.com/declare-lab/dialogue-ustanding上获得。
The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines. Specifically, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental contextual controlling factors of different aspects of a dialogue. Such insights can inspire more effective dialogue understanding models, and provide support for future text generation approaches. The implementation pertaining to this work is available at https://github.com/declare-lab/dialogue-understanding.
