Similarity of sentences through comparison of syntactic trees with pairs of similar words

Durán, Karla; Rodríguez, José; Bravo, Maricela

doi:10.1109/iceee.2014.6978311

Cited by 2 publications

(1 citation statement)

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“…We use subtrees that are not leaves, and that cover less than half of the sentence length. Durán et al (2014); Cardon and Grabar (2020) fully use this approach to compare sentences in the same language.…”

Section: Generating Synthetic Divergencesmentioning

confidence: 99%

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

Briakou¹,

Carpuat²

2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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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 finegrained 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 finegrained 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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Section: Generating Synthetic Divergencesmentioning

confidence: 99%