T rans S -Driven Joint Learning Architecture for Implicit Discourse Relation Recognition (2020)

Abstract: Implicit discourse relation recognition is a challenging task due to the lack of connectives as strong linguistic clues. Previous methods primarily encode two arguments separately or extract the specific interaction patterns for the task, which have not fully exploited the annotated relation signal. Therefore, we propose a novel TransS-driven joint learning architecture to address the issues. Specifically, based on the multi-level encoder, we 1) translate discourse relations in low-dimensional embedding space (called TransS), which could mine the latent geometric structure information of argument-relation instances; 2) further exploit the semantic features of arguments to assist discourse understanding; 3) jointly learn 1) and 2) to mutually reinforce each other to obtain the better argument representations, so as to improve the performance of the task. Extensive experimental results on the Penn Discourse TreeBank (PDTB) show that our model achieves competitive results against several state-of-the-art systems.

## introduction discourse relation describes how two adjacent text units (e.g., clauses, sentences, and larger sentence groups) are connected logically to one another. a discourse relation instance is usually defined as a connective taking two arguments (as arg1 and arg2, respectively). implicit discourse relation recognition without explicit connectives @xcite is still a challenging problem of discourse analysis, which needs to infer the discourse relation from a specific context. it is beneficial to many downstream natural language processing (nlp) applications, such as machine translation @xcite ) and text summarization @xcite . ## the proposed model the implicit discourse relation recognition task is usually formalized as a classification problem. in this section, we give an overview of the transsdriven joint learning framework, which consists of four parts: embedding layer, multi-level encoder, latent geometric structure learning, and semantic feature learning, as shown in figure 1 . ## related work neural network-based models have shown great effectiveness in implicit discourse relation recognition. we give the analysis of mainly relevant work: ## conclusion in this paper, we propose a novel transs-driven joint learning neural network framework by optimizing the discourse argument representations to improve implicit discourse relation recognition. we interpret the discourse relations as translation in low-dimensional embedding space, which reflects the geometric structure of argument-relation, and also can obtain the richer argument representations based on the multi-level encoder. different from the conventional approaches only considering the semantic features, we jointly leverage the latent geometric structure information and the semantic features to optimize the argument representations, which could improve the semantic understanding of discourse. experimental results on the pdtb show the effectiveness of our model.