  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 cross-lingual 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.
