论文标题
一个统一的双视图模型，用于审查摘要和情感分类，并损失不一致
A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
论文作者
论文摘要
从用户评论中获取准确的摘要和情感是现代电子商务平台的重要组成部分。审查摘要旨在产生一个简明的摘要，描述审查的关键意见和情感，而情感分类旨在预测表明审查情绪态度的情感标签。为了有效利用审查摘要和情感分类任务中共享的情感信息，我们提出了一个新颖的双视图模型，共同改善了这两个任务的性能。在我们的模型中，编码器首先学习了审查的上下文表示，然后摘要解码器会通过单词生成评论摘要。之后，源视情感分类器使用编码的上下文表示来预测审查的情感标签，而摘要视情感分类器使用解码器隐藏状态来预测生成的摘要的情感标签。在培训期间，我们引入了不一致的损失，以惩罚这两个分类器之间的分歧。它有助于解码器生成一个摘要，以使评论具有一致的情感趋势，还可以帮助两个情绪分类者相互学习。来自不同领域的四个现实世界数据集的实验结果证明了我们模型的有效性。
Acquiring accurate summarization and sentiment from user reviews is an essential component of modern e-commerce platforms. Review summarization aims at generating a concise summary that describes the key opinions and sentiment of a review, while sentiment classification aims to predict a sentiment label indicating the sentiment attitude of a review. To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks. In our model, an encoder first learns a context representation for the review, then a summary decoder generates a review summary word by word. After that, a source-view sentiment classifier uses the encoded context representation to predict a sentiment label for the review, while a summary-view sentiment classifier uses the decoder hidden states to predict a sentiment label for the generated summary. During training, we introduce an inconsistency loss to penalize the disagreement between these two classifiers. It helps the decoder to generate a summary to have a consistent sentiment tendency with the review and also helps the two sentiment classifiers learn from each other. Experiment results on four real-world datasets from different domains demonstrate the effectiveness of our model.
