scc's Introduction

SCC (Skilled Chess Commentator)

Codes of models and data processing for paper "Automated Chess Commentator Powered by Neural Chess Engine, Hongyu Zang, Zhiwei Yu and Xiaojun Wan, ACL2019" (pdf) will be released in this repo.
(Zhiwei and me contribute equally in this paper.)

Updating

Data preparation
- Make sure you follow this repo to get the basic dataset in data/crawler
- Ask the permission from Jhamtani et.al for the distribution of annotation2.tsv, rules.txt, and their processed dataset with [train/test/valid]_[0/1/2].en for data pre-processing.
- Put the above file into data/mycrawler/data/, and run the scripts (to be detailed) in data/mycrawler/ to get data/mycrawler/data/[train/valid/test].pickle
Experiment environments
- Python 3.5, Tensorflow 1.8
- Necessary: GAC, alpha-zero-general
- In case you need: Arena, deep-pink, sunfish
Training
- to be detailed
Test the chess engine
- Install Arena in arena/. Get sunfish and deep-pink in corresponding folders. Replace with files already in the folders.
- Download the checkpoint use the links in chess-agent/SCC/links
- Run Arena and compete with our model by adding chess-agent/engine into Arena engines. (to be detailed)
Reproduce the results
- Get Data preparation Done.
- Download the checkpoint use the links in chess-agent/SCC/links
- cd chess-agent/
- python main.py -c mixall

Note

The codes and README is still updating, more details will be cleared. Please be patient.
You can check previous most related work about Chess Commentary (GAC).
We also use the dataset provided by GAC. You may need to require the permission of distributions of the processed data and scripts from Jhamtani et al. (see previous link).
We build our chess agent on alpha-zero-general. If you are interested, you can learn and extend this project.

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