论文标题
主题感知的多转化对话建模
Topic-Aware Multi-turn Dialogue Modeling
论文作者
论文摘要
在基于检索的多转化对话建模中，根据上下文话语中提取的显着特征选择最合适的响应仍然是一个挑战。随着对话的进行，话题级别的主题转变自然是通过连续的多转对话环境发生的。但是，所有已知的基于检索的系统都对利用本地主题单词进行上下文话语表示满意，但未能捕获话语级别的这种基本的全球主题感知线索。本文没有将主题n-gram话语作为处理目的的处理单元，而是提供了一种新颖的主题感知解决方案，用于多转向对话建模，该解决方案段，该解决方案片段并提取了一种不受欢迎的方式，以一种不受欢迎的方式提取主题感知的话语，因此所得的模型能够捕捉到在需求上的主题转移，从而有效地捕捉到在话题上的出色主题转移，从而有效地跟踪了跨越的对话。我们的主题感知建模是由新提出的无监督的主题感知分段算法和主题感知的双重意识匹配（TADAM）网络实施的，该网络将每个主题段与双重交叉注意的响应匹配。三个公共数据集的实验结果表明，Tadam可以胜过最先进的方法，尤其是在电子商务数据集中有3.3％的方法，该数据集具有明显的主题转移。
In the retrieval-based multi-turn dialogue modeling, it remains a challenge to select the most appropriate response according to extracting salient features in context utterances. As a conversation goes on, topic shift at discourse-level naturally happens through the continuous multi-turn dialogue context. However, all known retrieval-based systems are satisfied with exploiting local topic words for context utterance representation but fail to capture such essential global topic-aware clues at discourse-level. Instead of taking topic-agnostic n-gram utterance as processing unit for matching purpose in existing systems, this paper presents a novel topic-aware solution for multi-turn dialogue modeling, which segments and extracts topic-aware utterances in an unsupervised way, so that the resulted model is capable of capturing salient topic shift at discourse-level in need and thus effectively track topic flow during multi-turn conversation. Our topic-aware modeling is implemented by a newly proposed unsupervised topic-aware segmentation algorithm and Topic-Aware Dual-attention Matching (TADAM) Network, which matches each topic segment with the response in a dual cross-attention way. Experimental results on three public datasets show TADAM can outperform the state-of-the-art method, especially by 3.3% on E-commerce dataset that has an obvious topic shift.
