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Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation

arXiv 1909.00437 ↗First posted 1 Sep 2019cs.CLcited by 27

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Abstract (arXiv)

The recently proposed massively multilingual neural machine translation (NMT) system has been shown to be capable of translating over 100 languages to and from English within a single model. Its improved translation performance on low resource languages hints at potential cross-lingual transfer capability for downstream tasks. In this paper, we evaluate the cross-lingual effectiveness of representations from the encoder of a massively multilingual NMT model on 5 downstream classification and sequence labeling tasks covering a diverse set of over 50 languages. We compare against a strong baseline, multilingual BERT (mBERT), in different cross-lingual transfer learning scenarios and show gains in zero-shot transfer in 4 out of these 5 tasks.

Key facts. Cited by 27 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2019.

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Citing papers per year: 2021: 14; 2022: 7; 2023: 1; 2024: 5.

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  1. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  2. 2022mSLAM: Massively multilingual joint pre-training for speech and textcited by 108
  3. 2022Enhancing Cross-lingual Transfer by Manifold Mixupcited by 30
  4. 2024Reuse Your Rewards: Reward Model Transfer for Zero-Shot Cross-Lingual Alignmentcited by 26
  5. 2022Cross-Lingual Dialogue Dataset Creation via Outline-Based Generationcited by 23
  6. 2021nmT5 -- Is parallel data still relevant for pre-training massively multilingual language models?cited by 21
  7. 2021PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretrainingcited by 21
  8. 2021Improving Zero-shot Cross-lingual Transfer between Closely Related Languages by injecting Character-level Noisecited by 19
  9. 2021Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Datacited by 17
  10. 2021Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systemscited by 16
  11. 2021Modelling Latent Translations for Cross-Lingual Transfercited by 16
  12. 2021MergeDistill: Merging Pre-trained Language Models using Distillationcited by 13
15 more
  1. 2021Distilling Large Language Models into Tiny and Effective Students using pQRNNcited by 10
  2. 2021X-METRA-ADA: Cross-lingual Meta-Transfer Learning Adaptation to Natural Language Understanding and Question Answeringcited by 10
  3. 2021Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documentscited by 7
  4. 2022CrossAligner & Co: Zero-Shot Transfer Methods for Task-Oriented Cross-lingual Natural Language Understandingcited by 7
  5. 2021Parameter Differentiation based Multilingual Neural Machine Translationcited by 5
  6. 2022Towards Unsupervised Content Disentanglement in Sentence Representations via Syntactic Rolescited by 5
  7. 2024Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detectioncited by 5
  8. 2021On the Universality of Deep Contextual Language Modelscited by 4
  9. 2023The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translationcited by 4
  10. 2021Exploring Unsupervised Pretraining Objectives for Machine Translationcited by 3
  11. 2021Investigating Post-pretraining Representation Alignment for Cross-Lingual Question Answeringcited by 3
  12. 2022Multi-level Distillation of Semantic Knowledge for Pre-training Multilingual Language Modelcited by 3
  13. 2024Improving Cross-lingual Representation for Semantic Retrieval with Code-switchingcited by 1
  14. 2024A Comparison of Language Modeling and Translation as Multilingual Pretraining Objectivescited by 1
  15. 2024Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysiscited by 1