Boundary Detection with BERT for Span-level Emotion Cause Analysis

Emotion cause analysis (ECA) has been an emerging topic in natural language processing, which aims to identify the reasons behind a certain emotion expressed in the text. Most ECA methods intend to identify the clause which contains the cause of a given emotion, but such clause-level ECA (CECA) can be ambiguous and imprecise. In this paper, we aim at span-level ECA (SECA) by detecting the precise boundaries of text spans conveying accurate emotion causes from the given context. We formulate this task as sequence labeling and position identification problems and design two neural methods to solve them. Experiments on two benchmark ECA datasets show that the proposed methods substantially outperform the existing ECA models 1 .