Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation

• URL: http://arxiv.org/abs/2105.03953v1
• Date: Sun, 9 May 2021 14:49:07 GMT
• Title: Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation
• Authors: Zihan Liu, Genta Indra Winata, Pascale Fung
• Abstract summary: We present a continual pre-training framework on mBART to effectively adapt it to unseen languages. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline. Our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training.
• Score: 53.22775597051498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The data scarcity in low-resource languages has become a bottleneck to building robust neural machine translation systems. Fine-tuning a multilingual pre-trained model (e.g., mBART (Liu et al., 2020)) on the translation task is a good approach for low-resource languages; however, its performance will be greatly limited when there are unseen languages in the translation pairs. In this paper, we present a continual pre-training (CPT) framework on mBART to effectively adapt it to unseen languages. We first construct noisy mixed-language text from the monolingual corpus of the target language in the translation pair to cover both the source and target languages, and then, we continue pre-training mBART to reconstruct the original monolingual text. Results show that our method can consistently improve the fine-tuning performance upon the mBART baseline, as well as other strong baselines, across all tested low-resource translation pairs containing unseen languages. Furthermore, our approach also boosts the performance on translation pairs where both languages are seen in the original mBART's pre-training. The code is available at https://github.com/zliucr/cpt-nmt.

Related papers

• Boosting Unsupervised Machine Translation with Pseudo-Parallel Data [2.900810893770134]
  We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora and synthetic sentence pairs back-translated from monolingual corpora. We reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.
  arXiv  Detail & Related papers  (2023-10-22T10:57:12Z)
• Cross-Lingual Transfer Learning for Phrase Break Prediction with Multilingual Language Model [13.730152819942445]
  Cross-lingual transfer learning can be particularly effective for improving performance in low-resource languages. This suggests that cross-lingual transfer can be inexpensive and effective for developing TTS front-end in resource-poor languages.
  arXiv  Detail & Related papers  (2023-06-05T04:10:04Z)
• 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)
• Improving Multilingual Neural Machine Translation For Low-Resource Languages: French-, English- Vietnamese [4.103253352106816]
  This paper proposes two simple strategies to address the rare word issue in multilingual MT systems for two low-resource language pairs: French-Vietnamese and English-Vietnamese. We have shown significant improvements of up to +1.62 and +2.54 BLEU points over the bilingual baseline systems for both language pairs.
  arXiv  Detail & Related papers  (2020-12-16T04:43:43Z)
• 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)
• Anchor-based Bilingual Word Embeddings for Low-Resource Languages [76.48625630211943]
  Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs. This paper proposes a new approach for building BWEs in which the vector space of the high resource source language is used as a starting point.
  arXiv  Detail & Related papers  (2020-10-23T19:17:00Z)
• Reusing a Pretrained Language Model on Languages with Limited Corpora for Unsupervised NMT [129.99918589405675]
  We present an effective approach that reuses an LM that is pretrained only on the high-resource language. The monolingual LM is fine-tuned on both languages and is then used to initialize a UNMT model. Our approach, RE-LM, outperforms a competitive cross-lingual pretraining model (XLM) in English-Macedonian (En-Mk) and English-Albanian (En-Sq)
  arXiv  Detail & Related papers  (2020-09-16T11:37:10Z)
• Multilingual Denoising Pre-training for Neural Machine Translation [132.66750663226287]
  mBART is a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora. mBART is one of the first methods for pre-training a complete sequence-to-sequence model.
  arXiv  Detail & Related papers  (2020-01-22T18:59:17Z)

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.