https://github.com/jaywalnut310/label-propagation-with-seq2seq

Label-Propagation-with-Seq2Seq
https://github.com/jaywalnut310/label-propagation-with-seq2seq

Last synced: 24 days ago
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Label-Propagation-with-Seq2Seq

• Host: GitHub
• URL: https://github.com/jaywalnut310/label-propagation-with-seq2seq
• Owner: jaywalnut310
• Created: 2017-07-05T01:42:43.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2017-08-03T07:13:19.000Z (almost 9 years ago)
• Last Synced: 2025-01-02T21:38:12.526Z (over 1 year ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 4.26 MB
• Stars: 0
• Watchers: 4
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# Label-Propagation with Seq2Seq

## 0. Introduction

This project implements label propagation with seq2seq.

It applies [Neural Graph Machines](https://arxiv.org/abs/1703.04818) to Seq2Seq.

### Problem Settings

* Semi-supervised learning techniques such as label propagation are used to solve classification (finite-categories) problems.

* And these approaches produce improvements at some extent.

* However, these approaches are not applied well to solve continuous target (infinite-categories) problems. 

* Therefore, I want to tackle this problem using Neural Graph Machines in this project.

### Some Details

* I test the performance in Neural Machine Translation problem.

* For calculating distance between nodes, I use L1, L2, and Mahalanobis distance metrics.

* Presentation info is given in https://goo.gl/whAbB1

----

### You can explore the whole project code by following jupyter notebook codes.

> **toy_example.ipynb** : contruct 2D sinc function with biased parallel data and unbiased non-parallel data

> **preprocessing.ipynb** : preprocess sentences

> **graph_operations.ipynb** : construct graph from source sentences

> **neural_graph_machines-benchmark.ipynb** : Default Encoder-Attention-Decoder Neural Translation Model

> **neural_graph_machines.ipynb** : Neural Graph Machine Model

## 1. Data Preparation

Experiments are done with IWSLT English-Vietnamese data set.

You can download using **download.sh**

I also use monolingual data from http://www.manythings.org/anki/.

After that, run **preprocessing.ipynb**

## 2. Graph Construction

run **graph_operations.ipynb**

## 3. Experiments

run **neural_graph_machines-benchmark.ipynb** and **neural_graph_machines.ipynb**