论文标题
基于检索的对话框系统的会话单词嵌入
Conversational Word Embedding for Retrieval-Based Dialog System
论文作者
论文摘要
人类对话包含许多类型的信息，例如知识，常识和语言习惯。在本文中，我们提出了一种名为Pr-Embedding的对话单词嵌入方法，该方法利用对话对$ \ weled \ langle {post，回复} \ right \ rangle $以学习单词嵌入。与以前的作品不同，Pr-bedding使用来自两个不同语义空间的向量来表示帖子和答复中的单词。为了捕获两人之间的信息，我们首先介绍统计计算机翻译的单词对齐模型以生成跨句子窗口，然后在单词级别和句子级别上训练嵌入式。我们评估了基于检索的对话系统的单转弯和多转弯响应选择任务的方法。实验结果表明，pr缩合可以提高所选响应的质量。 Pr-Embedding源代码可从https://github.com/wtma/pr-embedding获得
Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs $ \left\langle{post, reply} \right\rangle$ to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply. To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, then train the embedding on word-level and sentence-level. We evaluate the method on single-turn and multi-turn response selection tasks for retrieval-based dialog systems. The experiment results show that PR-Embedding can improve the quality of the selected response. PR-Embedding source code is available at https://github.com/wtma/PR-Embedding
