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Translation Aligned Sentence Embeddings for Turkish Language

arXiv 2311.09748 ↗First posted 16 Nov 2023cs.CLcited by 0

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Abstract (arXiv)

Due to the limited availability of high quality datasets for training sentence embeddings in Turkish, we propose a training methodology and a regimen to develop a sentence embedding model. The central idea is simple but effective : is to fine-tune a pretrained encoder-decoder model in two consecutive stages, where the first stage involves aligning the embedding space with translation pairs. Thanks to this alignment, the prowess of the main model can be better projected onto the target language in a sentence embedding setting where it can be fine-tuned with high accuracy in short duration with limited target language dataset.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 16 Nov 2023.

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23 of 29 references.

References · 23

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11 more
  1. 2020The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MTcited by 128
  2. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
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