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

arXiv: https://arxiv.org/abs/1601.03317 · HTML: https://mlsanity.com/paper/1601.03317/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2016.

## Built on by (most cited first, 1 of 1)

- [A New NMT Model for Translating Clinical Texts from English to Spanish](https://mlsanity.com/paper/2508.18607/) (2025)

## Abstract (arXiv)

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.