Levenshtein transformer (LevT) is a non-autoregressive machine translation model with high decoding efficiency and comparable translation quality in terms of bleu score, due to its parallel decoding and iterative refinement procedure. Are there any deficiencies of its translations and what improvements could be made? In this report, we focus on LevT's decoder and analyse the decoding results length, subword generation, and deletion module's capability. We hope to identify weaknesses of the decoder for future improvements. We also compare translations of the original LevT, knowledge-distilled LevT, LevT with translation memory, and the KD-LevT with translation memory to see how KD and translation memory can help.

翻译：莱文施泰因Transformer（LevT）是一种非自回归机器翻译模型，凭借其并行解码和迭代优化过程，具有较高的解码效率，在BLEU分数方面也达到了可比的翻译质量。其翻译是否存在不足，又能进行哪些改进？在本报告中，我们聚焦于LevT的解码器，分析了解码输出的长度、子词生成以及删除模块的能力。我们希望识别解码器的弱点以用于未来改进。我们还对原始LevT、知识蒸馏后的LevT、带翻译记忆的LevT以及带翻译记忆的KD-LevT的翻译结果进行了比较，以观察知识蒸馏和翻译记忆如何发挥作用。