Exploring answer stance detection with recurrent conditional attention

Abstract

Detecting stance from certain types of question-answer pairs is an interesting problem which has not been carefully explored. Unlike previous stance detection tasks, targets here are not given entities or claims but entire questions, which makes it difficult to capture the semantics of targets and build target-dependent representations of answers. To address them, we introduce the Recurrent Conditional Attention (RCA) model which incorporates a conditional attention structure into the recurrent reading process. RCA iteratively guides the distillation of question semantic with answer information and collects stance-oriented text relating to question, further revealing mutual relationship among stance, answer and question. Experiments on a manually labeled Chinese community QA stance dataset show that RCA outperforms four strong baselines by average 2.90% on macro-F1 and 2.66% on micro-F1 respectively.