A Comparative Study of Relation Classification Approaches for Japanese Discourse Relation Analysis

Abstract

This paper presents a comparative analysis of classification approaches in the Japanese discourse relation analysis (DRA) task. In the Japanese DRA task, it is difficult to resolve implicit relations where explicit discourse phrases do not appear. To understand implicit relations further, we compared the four approaches by incorporating a special token to encode the relations of the given discourses. Our four approaches included inserting a special token at the beginning of a sentence, end of a sentence, conjunctive position, and random position to classify the relation between the two discourses into one of the following categories: CAUSE/REASON, CONCESSION, CONDITION, PURPOSE, GROUND, CONTRAST, and NONE. Our experimental results revealed that special tokens are available to encode the relations of given discourses more effectively than pooling-based approaches. In particular, the random insertion of a special token outperforms other approaches, including pooling-based approaches, in the most numerous CAUSE/REASON category in implicit relations and categories with few instances. Moreover, we classified the errors in the relation analysis into three categories: confounded phrases, ambiguous relations, and requiring world knowledge for further improvements.