Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis
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Abstract (arXiv)
Learning chess strategies has been investigated widely, with most studies focussing on learning from previous games using search algorithms. Chess textbooks encapsulate grandmaster knowledge, explain playing strategies and require a smaller search space compared to traditional chess agents. This paper examines chess textbooks as a new knowledge source for enabling machines to learn how to play chess -- a resource that has not been explored previously. We developed the LEAP corpus, a first and new heterogeneous dataset with structured (chess move notations and board states) and unstructured data (textual descriptions) collected from a chess textbook containing 1164 sentences discussing strategic moves from 91 games. We firstly labelled the sentences based on their relevance, i.e., whether they are discussing a move. Each relevant sentence was then labelled according to its sentiment towards the described move. We performed empirical experiments that assess the performance of various transformer-based baseline models for sentiment analysis. Our results demonstrate the feasibility of employing transformer-based sentiment analysis models for evaluating chess moves, with the best performing model obtaining a weighted micro F_1 score of 68%. Finally, we synthesised the LEAP corpus to create a larger dataset, which can be used as a solution to the limited textual resource in the chess domain.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 30 of its 52 references that are in the map. First posted on arXiv 31 Oct 2023.
Builds on
30 of 52 references.
References · 30
- 2017Attention Is All You Needcited by 60,361
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
- 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,148
- 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
- 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,790
- 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
- 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 861
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
18 more
- 2020Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stoppingcited by 384
- 2018SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inferencecited by 333
- 2021Generating Datasets with Pretrained Language Modelscited by 193
- 2019BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysiscited by 176
- 2015Listen, Attend, and Walk: Neural Mapping of Navigational Instructions to Action Sequencescited by 50
- 2019The Materials Science Procedural Text Corpus: Annotating Materials Synthesis Procedures with Shallow Semantic Structurescited by 49
- 2014Learning to Win by Reading Manuals in a Monte-Carlo Frameworkcited by 39
- 2020The Chess Transformer: Mastering Play using Generative Language Modelscited by 37
- 2020SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semanticscited by 31
- 2019SemEval-2015 Task 10: Sentiment Analysis in Twittercited by 16
- 2017DeepChess: End-to-End Deep Neural Network for Automatic Learning in Chesscited by 14
- 2018Multimodal Grounding for Language Processingcited by 14
- 2018Extracting Action Sequences from Texts Based on Deep Reinforcement Learningcited by 12
- 2019SentiMATE: Learning to play Chess through Natural Language Processingcited by 9
- 2020Domain-Level Explainability -- A Challenge for Creating Trust in Superhuman AI Strategiescited by 5
- 2022RaFoLa: A Rationale-Annotated Corpus for Detecting Indicators of Forced Labourcited by 5
- 2023Do You Hear The People Sing? Key Point Analysis via Iterative Clustering and Abstractive Summarisationcited by 5
- 2019Comparing Knowledge-based Reinforcement Learning to Neural Networks in a Strategy Gamecited by 2
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