论文标题
基于检索的聊天机器人中多转响应选择的顺序句子匹配网络
Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots
论文作者
论文摘要
最近，开放式域多转弯聊天机器人吸引了许多学术界和行业研究人员的兴趣。基于检索的主要检索方法使用上下文响应匹配机制进行多转响应选择。具体而言，最新方法通过单词或段相似性执行上下文响应匹配。但是，这些模型缺乏对句子级别的语义信息的全面利用，并犯了人类很容易避免的简单错误。在这项工作中，我们提出了一个匹配的网络，称为顺序句子匹配网络（S2M），以使用句子级的语义信息来解决问题。首先，最重要的是，我们发现，通过使用句子级的语义信息，该网络成功地解决了该问题，并在匹配方面得到了重大改进，从而导致了最新的性能。此外，我们整合了这里介绍的句子匹配，并在当前文献中报告了通常的单词相似性匹配，以在不同的语义层面上进行匹配。三个公共数据集的实验表明，这种集成进一步改善了模型性能。
Recently, open domain multi-turn chatbots have attracted much interest from lots of researchers in both academia and industry. The dominant retrieval-based methods use context-response matching mechanisms for multi-turn response selection. Specifically, the state-of-the-art methods perform the context-response matching by word or segment similarity. However, these models lack a full exploitation of the sentence-level semantic information, and make simple mistakes that humans can easily avoid. In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem. Firstly and most importantly, we find that by using the sentence-level semantic information, the network successfully addresses the problem and gets a significant improvement on matching, resulting in a state-of-the-art performance. Furthermore, we integrate the sentence matching we introduced here and the usual word similarity matching reported in the current literature, to match at different semantic levels. Experiments on three public data sets show that such integration further improves the model performance.
