Rapid Adaptation of Neural Machine Translation to New Languages
This paper addresses the significant challenge of adapting neural machine translation (NMT) systems to low-resource languages (LRLs) efficiently and effectively. The authors present methodologies centered around the initiation with massively multilingual seed models, which are fine-tuned using data pertinent to the LRL of interest. These seed models are trained across a diverse range of languages beforehand, enabling them to perform surprisingly well on LRLs even without direct exposure to data in these languages.
A key proposal in this paper is similar-language regularization. This technique involves joint training on both the LRL and a closely related high-resource language (HRL) during adaptation to minimize overfitting risks associated with the limited LRL data. The experiments conducted demonstrate that even without direct adaptation, multilingual models achieve non-trivial BLEU scores, such as up to 15.5 for some language pairs, without any LRL training data. The similar-language regularization enhances this further by an average of 1.7 BLEU points over different settings involving four LRLs.
The paper explores the interplay of cross-lingual transfer learning and multilingual training. Three multilingual modeling strategies are extensively evaluated: single-source modeling, bi-source modeling, and all-source modeling. The single-source focuses solely on the LRL, while bi-source utilizes both a LRL and a related HRL. All-source modeling trains on all available language data, offering a universal model with extensive language coverage.
Fine-tuning from a universal model, as opposed to bilingual adaptation strategies, is highlighted as the most effective for both the warm-start scenario (where some data in the target language exists) and the cold-start scenario (where no such data is available at model creation time). The paper reveals that in cold-start scenarios, universal models can yield substantial translation results even without explicit exposure to LRL data, sometimes outperforming bi-source models.
Regarding adaptation methods, the paper shows that coupling similar-language regularization with standard fine-tuning significantly mitigates the risk of overfitting. Importantly, regularization was implemented in two ways: corpus concatenation, which simply merges datasets, and balanced sampling, where training batches are picked alternately from the LRL and the HRL according to a set ratio. Corpus concatenation emerged as the more effective strategy in the outlined scenarios.
Experiments were carried out on a 58-language pair dataset, featuring TED talks that cover a range of languages across multiple families. Languages such as Azerbaijani, Belarusian, Galician, and Slovak were paired with related HRLs—Turkish, Russian, Portuguese, and Czech, respectively. This experimental setup not only validated the efficacy of the proposed models but also pinpointed single-model universal training as a feasible and efficient approach to achieving satisfactory translation performance for low-resource scenarios.
Theoretical implications pertain to potential applications in rapid-response translation systems, particularly crucial in emergency situations involving rare languages. Practically, this research outlines a viable path for leveraging pre-existing multilingual models to deploy a functional MT system with minimal additional resources rapidly.
In conclusion, the paper successfully demonstrates the potential for massive multilingual NMT models combined with strategic regularization to provide effective solutions in low-resource language scenarios. This contributes significantly to ongoing efforts in making machine translation accessible and efficient across diverse linguistic landscapes, with implications for further developments exploring parameter sharing and cross-lingual knowledge transfer in broader AI applications.