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

• URL: http://arxiv.org/abs/2010.03662v1
• Date: Wed, 7 Oct 2020 21:26:20 GMT
• Title: Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank
• Authors: Eleftheria Briakou and Marine Carpuat
• Abstract summary: 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. Learning to rank helps detect fine-grained sentence-level divergences more accurately than a strong sentence-level similarity model.
• Score: 28.910206570036593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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