Multilingual Unsupervised Neural Machine Translation with Denoising Adapters

• URL: http://arxiv.org/abs/2110.10472v1
• Date: Wed, 20 Oct 2021 10:18:29 GMT
• Title: Multilingual Unsupervised Neural Machine Translation with Denoising Adapters
• Authors: Ahmet \"Ust\"un, Alexandre B\'erard, Laurent Besacier, Matthias Gall\'e
• Abstract summary: We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data. For this problem the standard procedure so far to leverage the monolingual data is back-translation, which is computationally costly and hard to tune. In this paper we propose instead to use denoising adapters, adapter layers with a denoising objective, on top of pre-trained mBART-50.
• Score: 77.80790405710819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We consider the problem of multilingual unsupervised machine translation, translating to and from languages that only have monolingual data by using auxiliary parallel language pairs. For this problem the standard procedure so far to leverage the monolingual data is back-translation, which is computationally costly and hard to tune. In this paper we propose instead to use denoising adapters, adapter layers with a denoising objective, on top of pre-trained mBART-50. In addition to the modularity and flexibility of such an approach we show that the resulting translations are on-par with back-translating as measured by BLEU, and furthermore it allows adding unseen languages incrementally.

Related papers

• LANDeRMT: Detecting and Routing Language-Aware Neurons for Selectively Finetuning LLMs to Machine Translation [43.26446958873554]
  Large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. Recent advancements in large language models (LLMs) have shown promising results in multilingual translation even with limited bilingual supervision. LandeRMT is a framework that selectively finetunes LLMs to textbfMachine textbfTranslation with diverse translation training data.
  arXiv  Detail & Related papers  (2024-09-29T02:39:42Z)
• On the Copying Problem of Unsupervised NMT: A Training Schedule with a Language Discriminator Loss [120.19360680963152]
  unsupervised neural machine translation (UNMT) has achieved success in many language pairs. The copying problem, i.e., directly copying some parts of the input sentence as the translation, is common among distant language pairs. We propose a simple but effective training schedule that incorporates a language discriminator loss.
  arXiv  Detail & Related papers  (2023-05-26T18:14:23Z)
• Language-Family Adapters for Low-Resource Multilingual Neural Machine Translation [129.99918589405675]
  Large multilingual models trained with self-supervision achieve state-of-the-art results in a wide range of natural language processing tasks. Multilingual fine-tuning improves performance on low-resource languages but requires modifying the entire model and can be prohibitively expensive. We propose training language-family adapters on top of mBART-50 to facilitate cross-lingual transfer.
  arXiv  Detail & Related papers  (2022-09-30T05:02:42Z)
• Parameter-Efficient Neural Reranking for Cross-Lingual and Multilingual Retrieval [66.69799641522133]
  State-of-the-art neural (re)rankers are notoriously data hungry. Current approaches typically transfer rankers trained on English data to other languages and cross-lingual setups by means of multilingual encoders. We show that two parameter-efficient approaches to cross-lingual transfer, namely Sparse Fine-Tuning Masks (SFTMs) and Adapters, allow for a more lightweight and more effective zero-shot transfer.
  arXiv  Detail & Related papers  (2022-04-05T15:44:27Z)
• DEEP: DEnoising Entity Pre-training for Neural Machine Translation [123.6686940355937]
  It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. We propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences.
  arXiv  Detail & Related papers  (2021-11-14T17:28:09Z)
• Multilingual Neural Machine Translation:Can Linguistic Hierarchies Help? [29.01386302441015]
  Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages. The performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. We propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer.
  arXiv  Detail & Related papers  (2021-10-15T02:31:48Z)
• Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution [16.939016405962526]
  We propose a straightforward vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models. Our approach is suitable for large-scale datasets, applies to distant languages with unseen scripts and incurs only minor degradation on the translation performance for the original language pairs.
  arXiv  Detail & Related papers  (2021-03-11T17:10:21Z)
• Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation [105.41167108465085]
  Cross-lingual Machine Reading (CLMRC) remains a challenging problem due to the lack of large-scale datasets in low-source languages. We propose a novel augmentation approach named Language Branch Machine Reading (LBMRC) LBMRC trains multiple machine reading comprehension (MRC) models proficient in individual language. We devise a multilingual distillation approach to amalgamate knowledge from multiple language branch models to a single model for all target languages.
  arXiv  Detail & Related papers  (2020-10-27T13:12:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.