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An Empirical Investigation of Multi-bridge Multilingual NMT models

arXiv 2110.07304 ↗First posted 14 Oct 2021cs.CLcited by 2

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Abstract (arXiv)

In this paper, we present an extensive investigation of multi-bridge, many-to-many multilingual NMT models (MB-M2M) ie., models trained on non-English language pairs in addition to English-centric language pairs. In addition to validating previous work which shows that MB-M2M models can overcome zeroshot translation problems, our analysis reveals the following results about multibridge models: (1) it is possible to extract a reasonable amount of parallel corpora between non-English languages for low-resource languages (2) with limited non-English centric data, MB-M2M models are competitive with or outperform pivot models, (3) MB-M2M models can outperform English-Any models and perform at par with Any-English models, so a single multilingual NMT system can serve all translation directions.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 27 references that are in the map. First posted on arXiv 14 Oct 2021.

Builds on

22 of 27 references.

References · 22

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10 more
  1. 2019Massively Multilingual Neural Machine Translationcited by 246
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Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communicationcited by 4
  2. 2023Exploring Linguistic Similarity and Zero-Shot Learning for Multilingual Translation of Dravidian Languagescited by 0