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

arXiv: https://arxiv.org/abs/2010.03662 · HTML: https://mlsanity.com/paper/2010.03662/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 18 of 18)

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- [Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation](https://mlsanity.com/paper/2105.15087/) (2021)
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- [Not all Fake News is Written: A Dataset and Analysis of Misleading Video Headlines](https://mlsanity.com/paper/2310.13859/) (2023)
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- [BitextEdit: Automatic Bitext Editing for Improved Low-Resource Machine Translation](https://mlsanity.com/paper/2111.06787/) (2021)
- [X-PARADE: Cross-Lingual Textual Entailment and Information Divergence across Paragraphs](https://mlsanity.com/paper/2309.08873/) (2023)
- [Measuring Fine-Grained Semantic Equivalence with Abstract Meaning Representation](https://mlsanity.com/paper/2210.03018/) (2022)
- [Can Synthetic Translations Improve Bitext Quality?](https://mlsanity.com/paper/2203.07643/) (2022)
- [Characterizing and Measuring Linguistic Dataset Drift](https://mlsanity.com/paper/2305.17127/) (2023)

## Abstract (arXiv)

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.