  Detecting fine-grained differences in content conveyed in different languages
matters for cross-lingual NLP and multilingual corpora analysis, but it is a
challenging machine learning problem since annotation is expensive and hard to
scale. This work improves the prediction and annotation of fine-grained
semantic divergences. We introduce a training strategy for multilingual BERT
models by learning to rank synthetic divergent examples of varying granularity.
We evaluate our models on the Rationalized English-French Semantic Divergences,
a new dataset released with this work, consisting of English-French
sentence-pairs annotated with semantic divergence classes and token-level
rationales. Learning to rank helps detect fine-grained sentence-level
divergences more accurately than a strong sentence-level similarity model,
while token-level predictions have the potential of further distinguishing
between coarse and fine-grained divergences.
