Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion
Lingzhi Wang, Xingshan Zeng, Kam‐Fai Wong
Abstract
Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for humans. This work studies automatic quotation recommendation for online conversations. Unlike the previous works that only consider semantic-level modeling, we adopt topic-level representation to facilitate the recommendation. A hierarchical architecture that is based on a pretrained language model is adopted to model the semantic-level conversation representation, and a neural topic model is employed to learn the topic-level representation. Moreover, the semantic-level conversation modeling is enhanced by a topic-aware attention mechanism, which is adopted to capture the interactive conversation structure from the perspective of word co-occurrence. The joint training of semantic- and topic-based recommendation leads to significantly better performance than the state-of-the-art models on two large-scale datasets. Apart from the novel and advanced recommendation framework, we conduct extensive quantitative experiments to investigate the difficulty of the quotation recommendation task, validate the topic-based recommendation assumption, and explore the stability of the recommendation. Some qualitative experiments and analyses are also included to interpret the quotation and topic distribution for some instances. All the extensive experiments and analyses provide persuasive explanations and interpretations of the module design and the recommendation results.