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Neural machine translation has shown very promising results lately. Most NMTmodels follow the encoder-decoder framework. To make encoder-decoder modelsmore flexible, attention mechanism was introduced to machine translation andalso other tasks like speech recognition and image captioning. We observe thatthe quality of translation by attention-based encoder-decoder can besignificantly damaged when the alignment is incorrect. We attribute theseproblems to the lack of distortion and fertility models. Aiming to resolvethese problems, we propose new variations of attention-based encoder-decoderand compare them with other models on machine translation. Our proposed methodachieved an improvement of 2 BLEU points over the original attention-basedencoder-decoder.

Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model