A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness
This work addresses a challenging task in sentiment analysis for researchers and practitioners, but it is incremental as it builds on prior two-stage approaches.
The paper tackles the problem of emotion-cause pair extraction (ECPE) in sentiment analysis by proposing a Dual-Questioning Attention Network to address error propagation and lack of contextual information in existing two-stage methods, achieving better performance than baselines on multiple evaluation metrics.
Emotion-cause pair extraction (ECPE), an emerging task in sentiment analysis, aims at extracting pairs of emotions and their corresponding causes in documents. This is a more challenging problem than emotion cause extraction (ECE), since it requires no emotion signals which are demonstrated as an important role in the ECE task. Existing work follows a two-stage pipeline which identifies emotions and causes at the first step and pairs them at the second step. However, error propagation across steps and pair combining without contextual information limits the effectiveness. Therefore, we propose a Dual-Questioning Attention Network to alleviate these limitations. Specifically, we question candidate emotions and causes to the context independently through attention networks for a contextual and semantical answer. Also, we explore how weighted loss functions in controlling error propagation between steps. Empirical results show that our method performs better than baselines in terms of multiple evaluation metrics. The source code can be obtained at https://github.com/QixuanSun/DQAN.