This article takes the lead to study aspect-level sentiment modification (ALSM) without parallel data. Given a sentence, the task of ALSM needs to reverse the sentiment with respect to the given aspect while preserving other content. The main challenge is reversing the sentiment of the given aspect without affecting the sentiments of other aspects in the sentences. To handle this problem, we propose a joint aspect-level sentiment modification (called JASM) model. JASM is a multitask system, which jointly trains two coupled modules: aspect-specific sentiment words extraction and aspect-level sentiment transformation. Besides, we propose a novel memory mechanism to learn aspect-aware sentiment representation and a gating mechanism to dynamically select aspect-aware sentiment information or content information for generating the next words. Experiments show that the proposed model substantially outperforms the compared methods in both aspect-level sentiment transformation and content preservation. For applications, we conduct data augmentation for aspect-based sentiment analysis (ABSA) through generating plausible training data with the trained ALSM model. Experiments show that data augmentation with generated data boosts the performance of a broad range of ABSA models.

中文翻译：

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在没有并行数据的情况下实现面向方面的情感修改

本文带头研究没有并行数据的方面层面的情感修饰（ALSM）。给定一个句子，ALSM的任务需要在保留其他内容的同时扭转与给定方面有关的观点。主要的挑战是在不影响句子中其他方面的情感的情况下逆转给定方面的情感。为了解决这个问题，我们提出了一个联合方面的情感修改（称为JASM）模型。JASM是一个多任务系统，它联合训练两个耦合的模块：特定方面的情感词提取和方面级别的情感转换。此外，我们提出了一种新颖的存储机制来学习感知方面的情感表示，并提出一种门控机制来动态选择感知方面的情感信息或内容信息以生成下一个单词。实验表明，所提出的模型在方面层面的情感转换和内容保存方面均明显优于所比较的方法。对于应用程序，我们通过使用训练有素的ALSM模型生成合理的训练数据来进行基于方面的情感分析（ABSA）的数据增强。实验表明，使用生成的数据进行数据增强可以提高各种ABSA模型的性能。