On the Effect of Word Order on Cross-lingual Sentiment Analysis

Alex R. Atrio, Toni Badia, Jeremy Barnes


Current state-of-the-art models for sentiment analysis make use of word order either explicitly by pre-training on a language modeling objective or implicitly by using recurrent neural networks (Rnns) or convolutional networks (Cnns). This is a problem for cross-lingual models that use bilingual embeddings as features, as the difference in word order between source and target languages is not resolved. In this work, we explore reordering as a pre-processing step for sentence-level crosslingual sentiment classification with two language combinations (English-Spanish, English-Catalan). We find that while reordering helps both models, Cnns are more sensitive to local reorderings, while global reordering benefits Rnns.

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