This paper deals with cross-lingual sentiment analysis in Czech, English and French languages. We perform zero-shot cross-lingual classification using five linear transformations combined with LSTM and CNN based classifiers. We compare the performance of the individual transformations, and in addition, we confront the transformation-based approach with existing state-of-the-art BERT-like models. We show that the pre-trained embeddings from the target domain are crucial to improving the cross-lingual classification results, unlike in the monolingual classification, where the effect is not so distinctive.
翻译:本文用捷克语、英语和法语进行跨语言的情绪分析。我们使用五个线性变换,加上LSTM和CNN的分类,进行零点跨语言分类。我们比较了个人变换的绩效,此外,我们面对基于变换的方法与现有的最先进的BERT类似模式。我们表明,目标领域的预先培训嵌入对于改进跨语言分类结果至关重要,这与单一语言的分类不同,因为单一语言的分类效果并不特别明显。