  Text classification is a fundamental task for text data mining. In order to
train a generalizable model, a large volume of text must be collected. To
address data insufficiency, cross-lingual data may occasionally be necessary.
Cross-lingual data sources may however suffer from data incompatibility, as
text written in different languages can hold distinct word sequences and
semantic patterns. Machine translation and word embedding alignment provide an
effective way to transform and combine data for cross-lingual data training. To
the best of our knowledge, there has been little work done on evaluating how
the methodology used to conduct semantic space transformation and data
combination affects the performance of classification models trained from
cross-lingual resources. In this paper, we systematically evaluated the
performance of two commonly used CNN (Convolutional Neural Network) and RNN
(Recurrent Neural Network) text classifiers with differing data transformation
and combination strategies. Monolingual models were trained from English and
French alongside their translated and aligned embeddings. Our results suggested
that semantic space transformation may conditionally promote the performance of
monolingual models. Bilingual models were trained from a combination of both
English and French. Our results indicate that a cross-lingual classification
model can significantly benefit from cross-lingual data by learning from
translated or aligned embedding spaces.
