A Study of Reinforcement Learning for Neural Machine Translation (1808.08866v1)

Abstract: Recent studies have shown that reinforcement learning (RL) is an effective approach for improving the performance of neural machine translation (NMT) system. However, due to its instability, successfully RL training is challenging, especially in real-world systems where deep models and large datasets are leveraged. In this paper, taking several large-scale translation tasks as testbeds, we conduct a systematic study on how to train better NMT models using reinforcement learning. We provide a comprehensive comparison of several important factors (e.g., baseline reward, reward shaping) in RL training. Furthermore, to fill in the gap that it remains unclear whether RL is still beneficial when monolingual data is used, we propose a new method to leverage RL to further boost the performance of NMT systems trained with source/target monolingual data. By integrating all our findings, we obtain competitive results on WMT14 English- German, WMT17 English-Chinese, and WMT17 Chinese-English translation tasks, especially setting a state-of-the-art performance on WMT17 Chinese-English translation task.

Reinforcement Learning in Neural Machine Translation: A Comprehensive Analysis

The paper "A Study of Reinforcement Learning for Neural Machine Translation" investigates the efficacy and challenges of incorporating Reinforcement Learning (RL) into Neural Machine Translation (NMT) systems. This paper provides a thorough exploration of several RL strategies to improve NMT models' performance, particularly when dealing with large-scale datasets and deep models.

The primary motivation behind this research is the inherent mismatch between the maximum likelihood estimation (MLE) training objectives commonly used in NMT and the sequence-level evaluation metrics such as BLEU scores. RL presents a promising alternative by optimizing sequence-level objectives. However, applying RL effectively in real-world NMT systems poses significant challenges due to RL’s notorious instability and inefficiency.

Methodology and Findings

The paper evaluates RL strategies across various translation tasks—specifically, WMT14 English-German, WMT17 English-Chinese, and WMT17 Chinese-English. The key methodologies explored in the paper include:

1. Reward Computation: The paper compared two sampling strategies for generating hypotheses: beam search and multinomial sampling, alongside the usage of reward shaping. Empirical results indicate that multinomial sampling consistently outperforms beam search, suggesting that richer exploration, as facilitated by multinomial sampling, generates more effective training data diversity.
2. Variance Reduction of Gradient Estimation: The authors examined the role of baseline functions in reducing gradient estimation variance. Contrary to previous findings, their experiments suggest minimal utility in implementing baseline rewards for NMT tasks, possibly due to the concentrated probability mass in target-language distributions, which simplifies expectation estimation.
3. Combined Objectives of MLE and RL: The experiments revealed that a balanced combination of MLE and RL objectives improves stabilization during training and yields better performance. The optimal configuration appears to involve a moderate emphasis on the RL component, striking a balance between the objectives.
4. Incorporating Monolingual Data: The paper uniquely addresses leveraging both source-side and target-side monolingual data within the RL framework. Through the inventive use of pseudo-target sentences and back-translation methods, the paper demonstrates that integrating monolingual data can significantly enhance translation performance. The inclusion of monolingual data coupled with RL training resulted in state-of-the-art performances, notably achieving a BLEU score of 26.73 on the WMT17 Chinese-English task, surpassing the best existing models.

Practical and Theoretical Implications

The findings from this research have several implications for NMT and RL:

• Translation Quality Improvement: By highlighting optimal configurations and strategies for combining RL with traditional methods, the paper provides a blueprint for implementing state-of-the-art NMT systems capable of exploiting large datasets effectively.
• Monolingual Data Utilization: The strategies devised for integrating monolingile are exemplary, opening avenues for NMT development, especially in languages where bilingual data is scarce.
• Broader Context for RL Applications: These results contribute to insights regarding RL's application in sequence generation tasks, emphasizing the necessity of balancing exploration with exploitation for achieving performance gains.

Future Directions

The paper’s findings uncover potential areas for continued exploration:

• Experimentation with Other RL Algorithms: Further investigation into alternative RL methodologies, such as actor-critic paradigms or Q-learning, could yield additional enhancements in NMT frameworks.
• Real-World Applications: Extending this research to cover other complex tasks in natural language processing and beyond can validate the scalability and adaptability of the proposed methodologies.
• Deeper Analysis of Instability Sources: Understanding the fundamental causes of RL instability in NMT could lead to the development of more sophisticated and robust training methodologies.

In conclusion, this paper presents a detailed and comprehensive investigation into utilizing reinforcement learning for neural machine translation, providing valuable insights and practical methodologies for enhancing translation model performance. By open-sourcing the implementation and datasets, this paper also offers a valuable resource for the research community, facilitating further advancements in this critical field of AI.