Generating natural language descriptions for chess moves is a complex task due to its dependency on both the game state and pragmatic context. Traditional approaches either lack flexibility or fail to provide diverse commentary styles, making automated commentary generation a challenging problem.
- Requires understanding of both game state and contextual dependencies.
- Existing datasets are either limited in scale or lack diverse commentary styles.
- Rule-based methods: Rely on predefined strategies, limiting flexibility.
- Neural models: Constrained by small datasets, leading to less diverse and context-aware commentary.
- Incorporates additional features like move history, advantages, threats, and opportunities.
- Aims to generate more engaging, human-like, and educational commentary for players of all skill levels.
- Tools Used:
requestsfor fetching web pages.BeautifulSoupfor parsing and extracting relevant content.
- Source Website: GameKnot
- Dataset Statistics:
- Total dataset size: 11.6K games
- Move-commentary pairs extracted: 298K
We implemented two models for generating chess commentary:
- Uses a transformer-based architecture.
- Incorporates game state, past move history, and piece positioning.
- Achieved 83% accuracy with a loss of 1.09.
- Enhanced with additional features such as threat detection and positional advantages.
- Achieved 95% accuracy with a loss of 0.33.
| Model | Accuracy | Loss |
|---|---|---|
| Model 1 | 83% | 1.09 |
| Model 2 | 95% | 0.33 |
- Overall Classifier Accuracy: 74.25%
- Data Extraction: Collecting diverse move-commentary pairs from online sources.
- Data Cleaning: Removing noise and irrelevant commentary.
- Model Training: Fine-tuning models to improve coherence and relevance of generated commentary.
- Vasudevan, D. et al., "Learning to Generate Chess Game Commentary from Real-World Data," in Proceedings of ACL 2018, Link.