mlsanityData to 14 Oct 2026

Cross-lingual paraphrase identification

arXiv 2406.15066 ↗First posted 21 Jun 2024cs.CLcited by 0

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Abstract (arXiv)

The paraphrase identification task involves measuring semantic similarity between two short sentences. It is a tricky task, and multilingual paraphrase identification is even more challenging. In this work, we train a bi-encoder model in a contrastive manner to detect hard paraphrases across multiple languages. This approach allows us to use model-produced embeddings for various tasks, such as semantic search. We evaluate our model on downstream tasks and also assess embedding space quality. Our performance is comparable to state-of-the-art cross-encoders, with only a minimal relative drop of 7-10% on the chosen dataset, while keeping decent quality of embeddings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 24 references that are in the map. First posted on arXiv 21 Jun 2024.

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References · 18

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6 more
  1. 2021COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretrainingcited by 126
  2. 2019Learning Dense Representations for Entity Retrievalcited by 122
  3. 2019Beyond BLEU: Training Neural Machine Translation with Semantic Similaritycited by 84
  4. 2019Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmaxcited by 58
  5. 2020NUBIA: NeUral Based Interchangeability Assessor for Text Generationcited by 34
  6. 2021Improving Paraphrase Detection with the Adversarial Paraphrasing Taskcited by 19

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