# Neural Machine Translation by Jointly Learning to Align and Translate

**Authors:** Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio  
**arXiv:** [1409.0473](https://arxiv.org/abs/1409.0473)  
**Date:** 2014-09-01  
**Summary page:** https://www.summarizepaper.com/en/paper/1409.0473v1/

## Key points

- Focuses on developing a single neural network for translation performance enhancement
- Utilizes encoder-decoder models with an encoder converting source sentence into fixed-length vector and a decoder generating translated output
- Potential limitation of using fixed-length vectors noted by authors
- Proposed extension allows model to autonomously search for relevant segments of source sentence
- Achieved translation results comparable to established phrase-based systems in English-to-French tasks
- Qualitative analysis shows alignment consistency with human intuition
- Method enhances translation accuracy and aligns source language segments with target words more intuitively

## Summary

This approach focuses on developing a single neural network that can be collectively optimized to enhance translation performance. Unlike traditional statistical machine translation methods, which rely on multiple models for different tasks, this method utilizes encoder-decoder models. These models consist of an encoder responsible for converting a source sentence into a fixed-length vector and a decoder that generates the translated output. However, the authors note a potential limitation in using fixed-length vectors within this basic architecture. They suggest that it may hinder performance improvements. To address this issue, they propose an extension that allows the model to autonomously (soft-)search for relevant segments of the source sentence without explicitly defining them as hard segments. Through this innovative approach, the researchers achieve translation results comparable to those of established phrase-based systems in English-to-French translation tasks. Additionally, qualitative analysis conducted on the (soft-)alignments identified by the model demonstrates alignment consistency with human intuition. This finding highlights the effectiveness of the proposed method in enhancing translation accuracy and aligning source language segments with their corresponding target words more intuitively. In conclusion, this research provides valuable insights into advancing neural machine translation techniques and optimizing performance through innovative alignment strategies.