Neural Machine Translation in Linear Time

[flrngel]

https://arxiv.org/abs/1610.10099
aka ByteNet
paper from Deepmind

Notations

• s: source
• t: target

Abstract

Features

• (model feature) stacking decoder on top of encoder
• (training feature) decoder using dynamically unfolding mechanism
• (result feature) linear in sequence length, side steps excessive memorization

1. Introduction

• ByteNet is resolution preserving
  • side steps memorization and allows maximal bandwidth between encoder and decoder

2. Neural Translation Model

2.1. Desiderata

(Desiderata is latin word of disideratum, which means model's goal in this paper)

• run in parallel (reducing computation time)
• resolution preserving with no constant size
• path between input and output has to be short

3. ByteNet

3.1. Encoder-Decoder Stacking

• decoder is on top of encoder because to maximize the representational bandwidth

3.2. Dynamic Unfolding

• finding length |t| with Linear equation (a=1.2, b=0 in this paper)
  

3.4. Masked One-dimensional Convolutions

• use masking to prevent future tokens not to affect current token

3.5. Dilation

• dilation makes receptive field grow exponentially

4. Model Comparison

Todo

• Search about dilated convolution