基于双问题注意网络的上下文感知推理-原因对抽取
A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context Awareness
摘要：
情感-原因对抽取（ECPE）是情感分析领域的一个新兴课题，旨在抽取文本中的情感对及其对应的原因。这是一个比情绪原因提取（ECE）更具挑战性的问题，因为它不需要情绪信号，这被证明是ECE任务中的重要作用。现有的工作遵循两个阶段的管道，在第一步确定情绪和原因，并在第二步将它们配对。然而，跨步骤的错误传播和没有上下文信息的配对组合限制了有效性。因此，我们提出了一个双重提问注意力网络来缓解这些限制。具体来说，我们通过注意力网络独立地询问候选人的情绪和原因，以获得上下文和语义的答案。此外，我们探讨如何加权损失函数在控制步骤之间的错误传播。实证结果表明，我们的方法在多个评估指标方面优于基线。源代码可以在www.example.com上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
