论文标题
面具与焦点：通过学习概念进行对话建模
Mask & Focus: Conversation Modelling by Learning Concepts
论文作者
论文摘要
序列模型的序列试图捕获输入和输出序列中所有单词之间的相关性。尽管这对于单词之间的相关性确实很强，但这对于机器翻译非常有用，但对于对话建模而言，相关性通常在较抽象的层面上，它变得有问题。相比之下，人类倾向于关注对话背景中讨论的基本概念，并相应地产生反应。在本文中，我们试图通过以无监督的方式学习基本概念和响应来模仿这种响应，从而生成这种机制。所提出的模型（称为掩码\＆focus）将输入上下文映射到一系列概念，然后将其用于生成响应概念。上下文和响应概念一起产生了最终响应。为了自动从培训数据中学习上下文概念，我们在输入中\ emph {mask}单词，并观察掩盖对响应生成的影响。我们训练我们的模型，以了解有关上下文概念具有高度共同信息的响应概念，从而将模型引导到\ emph {focus}上。 Mask \＆Focus在几个既定的对话指标中都对现有基线进行了重大改进。
Sequence to sequence models attempt to capture the correlation between all the words in the input and output sequences. While this is quite useful for machine translation where the correlation among the words is indeed quite strong, it becomes problematic for conversation modelling where the correlation is often at a much abstract level. In contrast, humans tend to focus on the essential concepts discussed in the conversation context and generate responses accordingly. In this paper, we attempt to mimic this response generating mechanism by learning the essential concepts in the context and response in an unsupervised manner. The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts. Together, the context and the response concepts generate the final response. In order to learn context concepts from the training data automatically, we \emph{mask} words in the input and observe the effect of masking on response generation. We train our model to learn those response concepts that have high mutual information with respect to the context concepts, thereby guiding the model to \emph{focus} on the context concepts. Mask \& Focus achieves significant improvement over the existing baselines in several established metrics for dialogues.
