论文标题
线性转换用于跨语言分析
Linear Transformations for Cross-lingual Sentiment Analysis
论文作者
论文摘要
本文涉及捷克，英语和法语语言的跨语言分析。我们使用五个线性转换与LSTM和CNN基于CNN的分类器进行零射击跨语性分类。我们比较了单个转换的性能，此外，我们与现有的类似伯特的模型面对基于转换的方法。我们表明，与单语言分类不同的是，来自目标域的预训练的嵌入对于改善跨语性分类结果至关重要，在单语分类中，效果并非如此独特。
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.
