论文标题
间接机器翻译对情感分类的影响
The Impact of Indirect Machine Translation on Sentiment Classification
论文作者
论文摘要
情感分类对于许多自然语言处理（NLP）应用至关重要，例如对电影评论，推文或客户反馈的分析。需要足够大量的数据来构建强大的情感分类系统。但是，这些资源并不总是用于所有域或所有语言。 在这项工作中，我们建议采用机器翻译（MT）系统将客户反馈转化为另一种语言，以调查哪种情况转换句子可能会对自动情感分类器产生正面或负面影响。此外，由于并非总是可以执行直接翻译，因此我们探讨了使用Pivot MT系统翻译的句子上自动分类器的性能。 我们使用上述方法进行了几项实验，以分析我们提出的情感分类系统的性能，并讨论分类翻译句子的优点和缺点。
Sentiment classification has been crucial for many natural language processing (NLP) applications, such as the analysis of movie reviews, tweets, or customer feedback. A sufficiently large amount of data is required to build a robust sentiment classification system. However, such resources are not always available for all domains or for all languages. In this work, we propose employing a machine translation (MT) system to translate customer feedback into another language to investigate in which cases translated sentences can have a positive or negative impact on an automatic sentiment classifier. Furthermore, as performing a direct translation is not always possible, we explore the performance of automatic classifiers on sentences that have been translated using a pivot MT system. We conduct several experiments using the above approaches to analyse the performance of our proposed sentiment classification system and discuss the advantages and drawbacks of classifying translated sentences.
