# Automated Chess Commentator Powered by Neural Chess Engine

arXiv: https://arxiv.org/abs/1909.10413 · HTML: https://mlsanity.com/paper/1909.10413/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation](https://mlsanity.com/paper/2109.06835/) (2021)
- [ChessQA: Evaluating Large Language Models for Chess Understanding](https://mlsanity.com/paper/2510.23948/) (2025)
- [Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation](https://mlsanity.com/paper/2410.20811/) (2024)
- [Understanding Game-Playing Agents with Natural Language Annotations](https://mlsanity.com/paper/2204.07531/) (2022)
- [From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary](https://mlsanity.com/paper/2506.17294/) (2025)
- [Exploring Collaboration between a language and a non-language agent](https://mlsanity.com/paper/2609.00474/) (2026)
- [Extreme Multi-Domain, Multi-Task Learning With Unified Text-to-Text Transfer Transformers](https://mlsanity.com/paper/2209.10106/) (2022)
- [Communicating Chess Strategies in Natural Language](https://mlsanity.com/paper/2607.11486/) (2026)
- [Hallucinations on the Board: Tool-Augmented Evaluation of LLM Chess Commentary](https://mlsanity.com/paper/2608.04240/) (2026)
- [Language Models that Play Chess and Explain Their Moves](https://mlsanity.com/paper/2610.03695/) (2026)

## Abstract (arXiv)

In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e.g., description, comparison, planning, etc.). We introduce a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. By jointly training the neural chess engine and the generation models for different categories, the models become more effective. We conduct experiments on 5 categories in a benchmark Chess Commentary dataset and achieve inspiring results in both automatic and human evaluations.