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A neural interlingua for multilingual machine translation

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arxiv 1804.08198 v3 pith:JEWDFJB3 submitted 2018-04-23 cs.CL

Yichao Lu , Phillip Keung , Faisal Ladhak , Vikas Bhardwaj , Shaonan Zhang , Jason Sun This is my paper
classification cs.CL
keywords neuraltranslationinterlinguamachinemultilingualapproacharchitecturebilingual
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We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a language-independent representation by performing direct zero-shot translation (without using pivot translation), and by using the source sentence embeddings to create an English Yelp review classifier that, through the mediation of the neural interlingua, can also classify French and German reviews. Furthermore, we show that, despite using a smaller number of parameters than a pairwise collection of bilingual NMT models, our approach produces comparable BLEU scores for each language pair in WMT15.

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Cited by 3 Pith papers

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  1. The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang

    cs.CL 2025-08 conditional novelty 7.0 of 10

    A novel constructed language with explicit grammar and dictionary reveals a large gap between human metalinguistic learning (87%) and the best LLM (47%) on translated CommonsenseQA.

  2. Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

    cs.CL 2019-07 unverdicted novelty 5.0 of 10

    A single multilingual NMT model for 103 languages trained on 25B examples demonstrates transfer learning benefits for low-resource languages.

  3. Improving Zero-shot Translation with Language-Independent Constraints

    cs.CL 2019-06 unverdicted novelty 4.0 of 10

    Language-independent constraints and regularization in multilingual Transformer NMT yield a 2.23 BLEU average gain on zero-shot pairs from the IWSLT 2017 dataset.

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