Minimum Risk Training for Neural Machine Translation (1512.02433v3)

Abstract: We propose minimum risk training for end-to-end neural machine translation. Unlike conventional maximum likelihood estimation, minimum risk training is capable of optimizing model parameters directly with respect to arbitrary evaluation metrics, which are not necessarily differentiable. Experiments show that our approach achieves significant improvements over maximum likelihood estimation on a state-of-the-art neural machine translation system across various languages pairs. Transparent to architectures, our approach can be applied to more neural networks and potentially benefit more NLP tasks.

• The paper introduces minimum risk training, replacing maximum likelihood estimation with a direct optimization method that focuses on evaluation metrics such as BLEU.
• It demonstrates architecture agnosticism, enabling application across various neural machine translation models without dependence on a specific design.
• Experimental results reveal significant improvements, including up to a 7.20 BLEU point increase in Chinese-English translations compared to traditional methods.

Minimum Risk Training for Neural Machine Translation: An Overview

The paper "Minimum Risk Training for Neural Machine Translation" presents an innovative approach to training neural machine translation (NMT) models. This method departs from the traditional maximum likelihood estimation (MLE) and introduces minimum risk training (MRT), which optimizes model parameters with respect to evaluation metrics rather than the likelihood of training data. MRT aligns more closely with the goal of improving translation quality, as it can incorporate evaluation metrics that may not be differentiable.

Key Contributions

1. Optimization with Evaluation Metrics: Unlike MLE, which focuses on maximizing the likelihood of training data, MRT is designed to minimize expected loss based on evaluation metrics such as BLEU. This allows for a more direct and meaningful optimization in the context of translation quality.
2. Architecture Agnosticism: MRT is not tied to any specific NMT architecture and can be applied to various models, expanding its potential usability across different neural network designs.
3. Utilization of Arbitrary Loss Functions: MRT handles non-differentiable, sentence-level loss functions, enabling the integration of diverse evaluation metrics that better capture the quality of translations.

Methodology

The paper establishes the theoretical framework for MRT by defining risk as the expected loss relative to the posterior distribution. It proposes sampling a subset from the full search space to approximate the posterior, making the calculation of expectations computationally feasible. The approach outlines a detailed process for sampling candidate translations, introducing efficiency in handling large datasets.

Experimental Results

The authors conducted experiments across three language pairs: Chinese-English, English-French, and English-German. The results indicate MRT's significant improvements over MLE, with the Chinese-English pair showing up to a 7.20 BLEU point increase:

• Chinese-English: MRT outperformed both MLE-driven NMT models and traditional statistical models like Moses, demonstrating superior translation quality.
• English-French and English-German: The method also achieved competitive results with state-of-the-art systems, albeit with slightly lesser margins compared to Chinese-English due to fewer reference translations and less structural divergence.

Implications and Future Directions

The introduction of MRT has important implications for improving NMT systems, particularly in handling structural divergences in languages and refining training objectives based on quality metrics. The flexibility of incorporating various architectures and loss functions suggests potential benefits beyond translation, extending to other NLP tasks.

Future work could explore applying MRT to more language combinations and extending it to other end-to-end neural systems. Additionally, integrating more advanced sampling strategies or enhancing the loss function's sensitivity could further improve the model's alignment with human evaluations.

In summary, the minimum risk training approach offers a promising direction for advancing neural machine translation, suggesting a shift from likelihood-based optimization towards more metric-centric methodologies that directly enhance translation outputs.