Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank [article]

Eleftheria Briakou, Marine Carpuat
2020 arXiv   pre-print
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
more » ... lized 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.
arXiv:2010.03662v1 fatcat:cqpq5cc7xfd7bc2ukkrfx5lwli