---
title: "Sequence to Sequence Learning with Neural Networks"
description: "Seq2Seq is the clean predecessor to attention and Transformers. It is a good beginner paper because the encoder-decoder contract is easy to visualize and still appears in translation, summarization, and agents."
canonical_url: "https://fanout.sh/daily/2026-07-07-sequence-to-sequence"
md_url: "https://fanout.sh/daily/2026-07-07-sequence-to-sequence.md"
last_updated: "2026-07-07"
access: "public"
---

# Sequence to Sequence Learning with Neural Networks

Seq2Seq is the clean predecessor to attention and Transformers. It is a good beginner paper because the encoder-decoder contract is easy to visualize and still appears in translation, summarization, and agents.

## Paper details

- Authors: Sutskever et al.

- Venue: NeurIPS 2014

- Track: AI Research

- Difficulty: Very Easy

- Reading time: 35 min

- Original paper: https://arxiv.org/abs/1409.3215

## What you will learn

- How one network encodes an input sequence and another decodes an output.

- Why fixed-size sequence representations created pressure for attention.

- How teacher forcing and beam search fit early neural translation.

## Continue exploring

[Explore Sequence Models](https://fanout.sh/knowledge-graph?node=ai-research%3Acategory%3Aglossary-architecture&detail=ai-research%3Aconcept%3Arnn): Open Fanout's sequence-modeling vocabulary and bridge RNNs, encoders, decoders, and later attention models.

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