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Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation

arXiv 2408.11926 ↗First posted 21 Aug 2024cs.CLcited by 0

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Abstract (arXiv)

Recent advancements in neural machine translation (NMT) have revolutionized the field, yet the dependency on extensive parallel corpora limits progress for low-resource languages and domains. Cross-lingual transfer learning offers a promising solution by utilizing data from high-resource languages but often struggles with in-domain NMT. This paper investigates zero-shot cross-lingual domain adaptation for NMT, focusing on the impact of domain specification and linguistic factors on transfer effectiveness. Using English as the source language and Spanish for fine-tuning, we evaluate multiple target languages, including Portuguese, Italian, French, Czech, Polish, and Greek. We demonstrate that both language-specific and domain-specific factors influence transfer effectiveness, with domain characteristics playing a crucial role in determining cross-domain transfer potential. We also explore the feasibility of zero-shot cross-lingual cross-domain transfer, providing insights into which domains are more responsive to transfer and why. Our results show the importance of well-defined domain boundaries and transparency in experimental setups for in-domain transfer learning.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 22 of its 33 references that are in the map. First posted on arXiv 21 Aug 2024.

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22 of 33 references.

References · 22

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10 more
  1. 2021MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfercited by 89
  2. 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
  3. 2016Zero-Resource Translation with Multi-Lingual Neural Machine Translationcited by 46
  4. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  5. 2016What to do about non-standard (or non-canonical) language in NLPcited by 35
  6. 2019Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translationcited by 23
  7. 2019Consistency by Agreement in Zero-shot Neural Machine Translationcited by 22
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  9. 2015Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairscited by 15
  10. 2022Robust Domain Adaptation for Pre-trained Multilingual Neural Machine Translation Modelscited by 2

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