Neural machine translation has shown very promising results lately. Most NMT
models follow the encoder-decoder framework. To make encoder-decoder models
more flexible, attention mechanism was introduced to machine translation and
also other tasks like speech recognition and image captioning. We observe that
the quality of translation by attention-based encoder-decoder can be
significantly damaged when the alignment is incorrect. We attribute these
problems to the lack of distortion and fertility models. Aiming to resolve
these problems, we propose new variations of attention-based encoder-decoder
and compare them with other models on machine translation. Our proposed method
achieved an improvement of 2 BLEU points over the original attention-based
encoder-decoder.

本文研究了采用注意力机制的编码器 - 解码器模型在机器翻译中的应用，发现当对齐不正确时，对翻译质量有明显影响。针对这些问题，提出了采用变形模型和受精度模型的新型方法，并在机器翻译中与其他模型进行了比较。该方法在原有的编码器 - 解码器基础上提高了 2BLEU 分数。