| Challenge: | Existing approaches to generate chess commentary are limited in template variety and are not precise enough. |
| Approach: | They propose a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. |
| Outcome: | The proposed model can be trained to generate chess commentary texts in 5 categories . the results are both automatic and human evaluations of the model . |
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Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data (P18-1)
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| Challenge: | Using a large-scale chess commentary dataset, we generate a set of comments for individual moves in a game. |
| Approach: | They propose a large-scale chess commentary dataset and a method to generate commentary for individual moves in a chessian game. |
| Outcome: | The proposed method is rated similar to ground truth commentary texts in terms of correctness and fluency. |
Bridging the Gap between Expert and Language Models: Concept-guided Chess Commentary Generation and Evaluation (2025.naacl-long)
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| Challenge: | Experimental results show that expert models generate accurate, informative and fluent commentary, but are prone to hallucinations due to their limited decision-making capabilities. |
| Approach: | They propose a concept-guided chess commentary generation and a GPT-based Chess Commentary Evaluation to bridge this gap between expert models and large language models. |
| Outcome: | The proposed model is accurate, informative, and fluent. |
Open-domain Video Commentary Generation (2022.emnlp-main)
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Edison Marrese-Taylor, Yumi Hamazono, Tatsuya Ishigaki, Goran Topić, Yusuke Miyao, Ichiro Kobayashi, Hiroya Takamura
| Challenge: | Existing approaches to generate live commentary on specific domains have been limited. |
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| Outcome: | The proposed model is based on well-known neural architectures and based off existing models. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
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| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |
Generating Textual Explanations for Machine Learning Models Performance: A Table-to-Text Task (2022.lrec-1)
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| Challenge: | Numerical tables are widely used to communicate or report the classification performance of machine learning models with respect to a set of evaluation metrics. |
| Approach: | They propose a task where neural models are trained to generate textual explanations based on the metrics’ scores reported in numerical tables. |
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Authorship Attribution for Neural Text Generation (2020.emnlp-main)
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| Challenge: | Recent advances in deep learning have enabled the generation of realistic artifacts . however, the qualities of texts generated by these models are better, often confusing classifiers if they are not real. |
| Approach: | They propose to use neural network-based language models to generate realistic texts . they investigate the authorship attribution problem in three versions of a text . |
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The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation (2023.acl-short)
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| Challenge: | Rather than train a model to obey these constraints implicitly, we opt to enforce them explicitly using a simple but novel approach to generation. |
| Approach: | They propose to automate the generation of sonnets within preset poetic constraints using a constrained decoding approach that uses a relatively modest neural backbone. |
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MOBA-E2C: Generating MOBA Game Commentaries via Capturing Highlight Events from the Meta-Data (2022.findings-emnlp)
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| Challenge: | e-sports game competitions lack commentators because of the shortage of professional human commentators. |
| Approach: | They propose a data-driven MOBA commentary generation framework for MOBA games . they use a rule-based generator and a generative GPT generator to generate commentaries . |
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Commentary Generation from Data Records of Multiplayer Strategy Esports Game (2024.naacl-srw)
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| Challenge: | Esports play logs are expensive for human experts to provide individual games with play-by-play commentaries. |
| Approach: | They first build large-scale esports data-to-text datasets that pair structured data and commentaries from a popular eSports game, League of Legends. |
| Outcome: | The proposed model can generate game commentaries from esports’ data records while examining the impact of the pre-trained language models. |
Automatic Generation of Large-scale Multi-turn Dialogues from Reddit (2022.coling-1)
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| Challenge: | Using a set of algorithms, we can generate large dialogue corpus from Reddit. |
| Approach: | They propose to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. |
| Outcome: | The proposed methods improve on the baseline method by 36.3% . the best method shows an improvement of 36.6% over the previous one . |