Source-linked AI summary
Rapid Adaptation of Neural Machine Translation to New Languages
Graham Neubig, Junjie Hu
TL;DR
The paper asks how neural machine translation systems can be adapted to new low-resource languages both accurately and rapidly. It starts from multilingual seed models and fine-tunes with related-language data, finding strong zero-shot performance and improved adaptation with similar-language regularization.
Problem
Neural machine translation for low-resource languages requires large datasets, while creating systems quickly is important for crisis situations.
Method
The paper trains multilingual seed models and fine-tunes them for a low-resource language, using similar-language regularization with a related high-resource language to prevent over-fitting.
Results
The universal-model-plus-SLR method improves over adapting only to the low-resource language by 1.7 BLEU points on average, while a 57-language model reaches up to 15.5 BLEU without new-language training data.
Takeaways & Limitations
Pre-trained universal models can support rapid adaptation to new low-resource languages, with similar-language regularization producing the strongest reported adaptation results.
Takeaways & Limitations
Universal models may lose language-specific accuracy under a limited parameter budget, and selecting a helper language remains open when typological and lexical similarity do not coincide.
Abstract
from arXiv · showhide
This paper examines the problem of adapting neural machine translation systems to new, low-resourced languages (LRLs) as effectively and rapidly as possible. We propose methods based on starting with massively multilingual "seed models", which can be trained ahead-of-time, and then continuing training on data related to the LRL. We contrast a number of strategies, leading to a novel, simple, yet effective method of "similar-language regularization", where we jointly train on both a LRL of interest and a similar high-resourced language to prevent over-fitting to small LRL data. Experiments demonstrate that massively multilingual models, even without any explicit adaptation, are surprisingly effective, achieving BLEU scores of up to 15.5 with no data from the LRL, and that the proposed similar-language regularization method improves over other adaptation methods by 1.7 BLEU points average over 4 LRL settings. Code to reproduce experiments at https://github.com/neubig/rapid-adaptation
1 Introduction
Neural machine translation for low-resource languages requires substantial data and can be too slow for crisis response. The paper proposes rapid adaptation from multilingual seed models, including similar-language regularization, and reports strong zero-shot and adapted performance.
- Low-resource neural machine translation requires large training datasets, making high-quality systems difficult to create.
- Training delays of days or weeks make conventional system creation impractical when disaster response requires rapid translation.
- The paper combines cross-lingual transfer learning and multilingual training by first training a multilingual seed model, then fine-tuning it for the language of interest.
- Similar-language regularization jointly uses a related high-resource language during adaptation to help prevent over-fitting to the small low-resource dataset.
- 15.5 BLEU is achieved by a single model trained on 57 languages without training data in the new source language.
- 1.7 BLEU points are gained on average over methods adapting only to the low-resource language.
2 Training Paradigms
The paper compares multilingual training and adaptation paradigms for translating a source low-resource language into English, balancing accuracy against rapid deployment. It focuses on tailoring broader models to specific languages while using related high-resource data to reduce over-fitting.
- Adaptation by Fine-tuning: Adaptation begins with larger multilingual training data, followed by fine-tuning to tailor a general model to the source LRL.The compared direction is from bi-source or universal models toward single-source or bi-source models.
- Multilingual Modeling Methods: Three training paradigms are compared: single-source uses only LRL-English data, bi-source adds a related HRL, and all-source trains on all available languages.The all-source experiments use 58 source languages.
- Assumption: Choosing a helper language assumes strong similarity in typology and lexical overlap, and selecting helpers outside that setting remains future work.The experiments pair Azerbaijani-Turkish, Belarusian-Russian, Galician-Portuguese, and Slovak-Czech.
- Evaluation Setting: The study evaluates two requirements for adaptation: translation accuracy and the training time from identifying a need to producing adequate results.Experiments use paired LRL-HRL settings in a 58-language-to-English TED corpus.
- Adaptation by Fine-tuning: Fine-tuning varies by seed-model variety and start condition: adaptation can begin from a bilingual or universal model, with either warm or cold access to LRL data.Warm start includes the LRL during original training; cold start receives LRL data only afterward.
- Similar-Language Regularization: Similar-language regularization adapts with both LRL and similar-HRL data to prevent over-fitting on the small LRL training set.The paper tests corpus concatenation and balanced sampling, including LRL-to-HRL ratios of 1-to-1, 1-to-2, and 1-to-4.
3 Experiments
Experiments evaluate multilingual training and adaptation across four language pairs using TED data, comparing warm-start and cold-start settings. Multilingual models substantially improve low-resource translation, while universal seed models and similar-language regularization support effective, rapid adaptation.
- Experimental Setup: Experiments use the 58-language-to-English TED corpus, evaluating Azerbaijani, Belarusian, Galician, and Slovak with Turkish, Russian, Portuguese, and Czech as similar HRLs.The models are implemented with attentional NMT and LSTM encoders and decoders.
- Experimental Setup: Table 2 reports BLEU for single-source, bi-source, and all-source universal models, including adapted counterparts in warm-start and cold-start scenarios.Balanced sampling variants are also included among the adapted models.
- Multilingual Training: 7-13 BLEU points are gained by moving from single-source to bi-source or all-source training in warm-start settings.Bi-source models tend to perform slightly better than all-source models when parameter capacity is identical.
- Multilingual Training: Multilingual training remedies the low-resource weakness of NMT, with all multilingual variants outperforming the phrase-based baseline.The phrase-based baseline is trained only on bilingual data.
- Cold-Start Results: 15.5 BLEU is achieved on glg-eng without target-language training data, while all-source models outperform bi-source models in cold-start settings.Unsupervised NMT achieves around 0 BLEU across all language pairs.
- Adaptation Results: 1.7 BLEU points are gained by similar-language regularization over direct LRL adaptation in cold-start settings.Corpus concatenation outperforms balanced sampling in both cold-start and warm-start scenarios; adapted models also converge faster than bi-source models trained from scratch.
4 Related Work
Related work establishes multilingual NMT as promising for crossing language boundaries and improving low-resource translation. This paper differs by focusing on adaptation to new languages and noting several approaches it does not examine.
- Multilingual NMT: Multilingual NMT is promising because it can abstract across language boundaries.Prior work includes multilingual systems developed by Firat et al., Ha et al., and Johnson et al.
- Research Gap: Previous low-resource multilingual translation studies demonstrate potential but do not address adaptation to new languages, the main focus here.The paper positions new-language adaptation as distinct from prior multilingual training work.
- Adjacent Approaches: Partial parameter freezing is not examined, although it has supported cross-lingual adaptation and could potentially complement multilingual training.The paper also mentions unsupervised NMT as a separate line of work.
5 Conclusion
The paper concludes that rapid adaptation is best achieved by fine-tuning a universal model while regularizing with similar languages. This holds in both warm-start and cold-start scenarios.
- Conclusion: Fine-tuning provides the paper’s approach to rapidly adapting MT systems to new languages.The conclusion frames adaptation as continuing training from an existing model.
- Conclusion: The best results in both warm-start and cold-start scenarios come from adapting a pre-trained universal model to the LRL while regularizing with similar languages.This combines universal pretraining with similar-language regularization.
- Conclusion: Similar-language regularization is the concluding strategy for adapting universal models to low-resource languages across both initialization settings.The conclusion applies the result to both warm-start and cold-start adaptation.