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arXiv · 2406.15066

Cross-lingual paraphrase identification

Inessa Fedorova·Aleksei Musatow
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Abstract

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.

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Inessa Fedorova, Aleksei Musatow. 2024-06-21. Cross-lingual paraphrase identification. https://arxiv.org/abs/2406.15066

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