  Cross-lingual text classification leverages text classifiers trained in a
high-resource language to perform text classification in other languages with
no or minimal fine-tuning (zero/few-shots cross-lingual transfer). Nowadays,
cross-lingual text classifiers are typically built on large-scale, multilingual
language models (LMs) pretrained on a variety of languages of interest.
However, the performance of these models vary significantly across languages
and classification tasks, suggesting that the superposition of the language
modelling and classification tasks is not always effective. For this reason, in
this paper we propose revisiting the classic "translate-and-test" pipeline to
neatly separate the translation and classification stages. The proposed
approach couples 1) a neural machine translator translating from the targeted
language to a high-resource language, with 2) a text classifier trained in the
high-resource language, but the neural machine translator generates "soft"
translations to permit end-to-end backpropagation during fine-tuning of the
pipeline. Extensive experiments have been carried out over three cross-lingual
text classification datasets (XNLI, MLDoc and MultiEURLEX), with the results
showing that the proposed approach has significantly improved performance over
a competitive baseline.
