# Neural Machine Translation and Sequence-to-sequence Models: A Tutorial

arXiv: https://arxiv.org/abs/1703.01619 · HTML: https://mlsanity.com/paper/1703.01619/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 25 of 25)

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- [Deliberation Networks and How to Train Them](https://mlsanity.com/paper/2211.03217/) (2022)
- [Low-Resource Neural Machine Translation Using Recurrent Neural Networks and Transfer Learning: A Case Study on English-to-Igbo](https://mlsanity.com/paper/2504.17252/) (2025)

## Abstract (arXiv)

This tutorial introduces a new and powerful set of techniques variously called "neural machine translation" or "neural sequence-to-sequence models". These techniques have been used in a number of tasks regarding the handling of human language, and can be a powerful tool in the toolbox of anyone who wants to model sequential data of some sort. The tutorial assumes that the reader knows the basics of math and programming, but does not assume any particular experience with neural networks or natural language processing. It attempts to explain the intuition behind the various methods covered, then delves into them with enough mathematical detail to understand them concretely, and culiminates with a suggestion for an implementation exercise, where readers can test that they understood the content in practice.