Identification of Cross-Document Sentence Relations from Document Pairs

Abstract

We propose a novel task that identifies cross-document sentence relations from document pairs. Although there are numerous studies that focus on finding sentence relations from just one document or conversation, only few studies are proposed for cross-documents. Examples of cross-document sentence relations are question–answer relations, request–response relations, and so on. Finding such relations will lead to many applications since the cross-document sentence relations are useful to explain document-based conversations on a more fine-grained level. For instance, we can extract communications from cross-documents by accumulating sentences having relations. To detect such relations, we regard this task as the classification problem and employ the conditional random fields. In particular, we modify a previous method that focuses on finding relations from conversations using sentence types to our task. Furthermore, we propose a combined model that simultaneously estimates sentence types and relations. The experiments are performed on review and reply on an internet service for hotel reservation, and the results show that our proposed model achieves 46.6% precision and 61.0% recall, which outperforms previous models.