Boosting Unsupervised Machine Translation with Pseudo-Parallel Data

• URL: http://arxiv.org/abs/2310.14262v1
• Date: Sun, 22 Oct 2023 10:57:12 GMT
• Title: Boosting Unsupervised Machine Translation with Pseudo-Parallel Data
• Authors: Ivana Kvapil\'ikov\'a and Ond\v{r}ej Bojar
• Abstract summary: 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.
• Score: 2.900810893770134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Even with the latest developments in deep learning and large-scale language modeling, the task of machine translation (MT) of low-resource languages remains a challenge. Neural MT systems can be trained in an unsupervised way without any translation resources but the quality lags behind, especially in truly low-resource conditions. We propose a training strategy that relies on pseudo-parallel sentence pairs mined from monolingual corpora in addition to synthetic sentence pairs back-translated from monolingual corpora. We experiment with different training schedules and reach an improvement of up to 14.5 BLEU points (English to Ukrainian) over a baseline trained on back-translated data only.

Related papers

• Relevance-guided Neural Machine Translation [5.691028372215281]
  We propose an explainability-based training approach for Neural Machine Translation (NMT) Our results show our method can be promising, particularly when training in low-resource conditions.
  arXiv  Detail & Related papers  (2023-11-30T21:52:02Z)
• Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning [48.15259834021655]
  We present a pragmatic approach towards building a multilingual machine translation model that covers hundreds of languages. We use a mixture of supervised and self-supervised objectives, depending on the data availability for different language pairs. We demonstrate that the synergy between these two training paradigms enables the model to produce high-quality translations in the zero-resource setting.
  arXiv  Detail & Related papers  (2022-01-09T23:36:44Z)
• Improving Multilingual Translation by Representation and Gradient Regularization [82.42760103045083]
  We propose a joint approach to regularize NMT models at both representation-level and gradient-level. Our results demonstrate that our approach is highly effective in both reducing off-target translation occurrences and improving zero-shot translation performance.
  arXiv  Detail & Related papers  (2021-09-10T10:52:21Z)
• Continual Mixed-Language Pre-Training for Extremely Low-Resource Neural Machine Translation [53.22775597051498]
  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.
  arXiv  Detail & Related papers  (2021-05-09T14:49:07Z)
• Learning Policies for Multilingual Training of Neural Machine Translation Systems [36.292020779233056]
  Low-resource Multilingual Neural Machine Translation (MNMT) is typically tasked with improving the translation performance on one or more language pairs. We propose two simple search based curricula, which help improve translation performance in conjunction with existing techniques such as fine-tuning.
  arXiv  Detail & Related papers  (2021-03-11T21:38:04Z)
• 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)
• Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation [54.52971020087777]
  Using monolingual data significantly boosts the translation quality of low-resource languages in multilingual models. Self-supervision improves zero-shot translation quality in multilingual models. We get up to 33 BLEU on ro-en translation without any parallel data or back-translation.
  arXiv  Detail & Related papers  (2020-05-11T00:20:33Z)
• Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios [61.88012735215636]
  Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks. In real-world scenarios, massive monolingual corpora do not exist for some extremely low-resource languages such as Estonian. We propose UNMT self-training mechanisms to train a robust UNMT system and improve its performance.
  arXiv  Detail & Related papers  (2020-04-09T12:07:17Z)
• Cross-lingual Supervision Improves Unsupervised Neural Machine Translation [97.84871088440102]
  We introduce a multilingual unsupervised NMT framework to leverage weakly supervised signals from high-resource language pairs to zero-resource translation directions. Method significantly improves the translation quality by more than 3 BLEU score on six benchmark unsupervised translation directions.
  arXiv  Detail & Related papers  (2020-04-07T05:46:49Z)

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.