The paper presents a comprehensive study on the effectiveness of sentence-level and token-level knowledge distillation in neural machine translation (NMT). The authors hypothesize that token-level distillation is more suitable for simpler scenarios, while sentence-level distillation excels in complex scenarios.
To validate this hypothesis, the authors conduct experiments by varying the size of the student model, the complexity of the text, and the difficulty of the decoding process. The results consistently show that token-level distillation performs better in simpler scenarios, such as those with larger student models, less complex text, and more available decoding information. Conversely, sentence-level distillation is more effective in complex scenarios, where the student model is smaller, the text is more complex, and the decoding process is more challenging.
To address the challenge of defining the complexity level of a given scenario, the authors propose a hybrid method that combines the advantages of both sentence-level and token-level distillation through a dynamic gating mechanism. This hybrid approach outperforms the individual distillation methods and various baseline models, demonstrating its effectiveness in navigating scenarios with ambiguous complexity levels.
The paper provides valuable insights into the strengths and limitations of different knowledge distillation techniques in NMT, and the proposed hybrid method offers a practical solution for enhancing translation quality across a wide range of scenarios.
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by Jingxuan Wei... at arxiv.org 04-24-2024
https://arxiv.org/pdf/2404.14827.pdfDeeper Inquiries