Detecting Fine-Grained Cross-Lingual Semantic Divergences without Supervision by Learning to Rank (2020.emnlp-main)
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| Challenge: | Detecting fine-grained differences in content conveyed in different languages is expensive and hard to scale. |
| Approach: | They propose a training strategy for multilingual BERT models by learning to rank divergent examples of varying granularity. |
| Outcome: | The proposed model improves the prediction and annotation of fine-grained semantic divergences. |
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