Fast Domain Adaptation for Neural Machine Translation (1612.06897v1)

Abstract: Neural Machine Translation (NMT) is a new approach for automatic translation of text from one human language into another. The basic concept in NMT is to train a large Neural Network that maximizes the translation performance on a given parallel corpus. NMT is gaining popularity in the research community because it outperformed traditional SMT approaches in several translation tasks at WMT and other evaluation tasks/benchmarks at least for some language pairs. However, many of the enhancements in SMT over the years have not been incorporated into the NMT framework. In this paper, we focus on one such enhancement namely domain adaptation. We propose an approach for adapting a NMT system to a new domain. The main idea behind domain adaptation is that the availability of large out-of-domain training data and a small in-domain training data. We report significant gains with our proposed method in both automatic metrics and a human subjective evaluation metric on two language pairs. With our adaptation method, we show large improvement on the new domain while the performance of our general domain only degrades slightly. In addition, our approach is fast enough to adapt an already trained system to a new domain within few hours without the need to retrain the NMT model on the combined data which usually takes several days/weeks depending on the volume of the data.

Fast Domain Adaptation for Neural Machine Translation

The paper "Fast Domain Adaptation for Neural Machine Translation" by Freitag and Al-Onaizan presents a novel approach to address the challenge of domain adaptation in Neural Machine Translation (NMT) systems. While the efficacy of NMT over Statistical Machine Translation (SMT) has been established in previous research, the integration of enhancements from SMT into NMT frameworks, particularly domain adaptation, remained incomplete. This paper proposes a fast and efficient methodology for adapting NMT systems to new domains without substantial degradation of translation quality in out-of-domain contexts.

Methodology and Approach

The core concept of the proposed approach is to continue training an existing NMT system, initially trained on a large amount of out-of-domain data, using a relatively small in-domain dataset. The technique leverages the already established baseline model, updating its parameters with data from the new domain, ensuring a more targeted adaptation that circumvents the extensive time requirements associated with training from scratch on combined datasets. This continued training, referred to as the "continue model," is followed by an ensemble decoding strategy that integrates the adapted model with the original baseline. This ensemble approach effectively mitigates the risk of overfitting and maintains quality across both in-domain and general domain translations.

Experimental Results

The efficacy of the proposed method is demonstrated through experiments on two translation tasks: German$\rightarrow$English and Chinese$\rightarrow$English. For the German$\rightarrow$English task, significant improvements were observed with gains up to 33.6 Bleu points after adapting the model with in-domain data, reducing overfitting through ensemble. In contrast, the baseline model trained only on the out-of-domain data achieved 29.2 Bleu points. Importantly, this adaptation process was expedited to within a few hours, a stark contrast to the potential weeks required for retraining on combined data. Similarly, for Chinese$\rightarrow$English translation, improvements of up to 10 points in Bleu while maintaining performance on out-of-domain test sets.

Human Evaluations

The paper also includes human evaluation results, providing qualitative assessments beyond automatic metrics. In these evaluations, both the continue and ensemble models outperform the baseline models on in-domain datasets, underscoring the practical effectiveness of the proposed method.

Implications and Future Work

The findings of this paper have considerable implications for the deployment of NMT systems across diverse domains, highlighting a scalable approach that balances time efficiency with performance integrity across different context requirements. The methodology promises to enhance adaptability in translation systems, facilitating wider applications in domain-specific content without demanding extensive computational resources for retraining.

Future research might explore the potential of integrating this domain adaptation strategy into various architectures and frameworks beyond those discussed. Moreover, analyzing the implications of this methodology in terms of computational resources and potential advancements in model ensembling could further optimize translation outputs for NMT systems. As the landscape of AI developments continues to evolve, the need for adaptive and efficient translation systems remains vital, and contributions such as these are significant in propelling the discipline forward.