title: Quotation Recommendation for Multi-party Online Conversations Based on Semantic and Topic Fusion
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
journal: ACM Transactions on Information Systems