Generation Challenges: Results of the Accuracy Evaluation Shared Task
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Abstract (arXiv)
The Shared Task on Evaluating Accuracy focused on techniques (both manual and automatic) for evaluating the factual accuracy of texts produced by neural NLG systems, in a sports-reporting domain. Four teams submitted evaluation techniques for this task, using very different approaches and techniques. The best-performing submissions did encouragingly well at this difficult task. However, all automatic submissions struggled to detect factual errors which are semantically or pragmatically complex (for example, based on incorrect computation or inference).
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 9 of its 11 references that are in the map. First posted on arXiv 12 Aug 2021.
Builds on
9 of 11 references.
References · 9
- 2017Challenges in Data-to-Document Generationcited by 226
- 2018Data-to-Text Generation with Content Selection and Planningcited by 106
- 2019Data-to-text Generation with Entity Modelingcited by 45
- 2020A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systemscited by 34
- 2020Revisiting Challenges in Data-to-Text Generation with Fact Groundingcited by 27
- 2019A Hierarchical Model for Data-to-Text Generationcited by 20
- 2019Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)cited by 18
- 2019Learning to Select, Track, and Generate for Data-to-Textcited by 14
- 2020Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verificationcited by 6
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Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 0; 2026: 1.
9 papers.
- 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Textcited by 154
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generationcited by 55
- 2022Data-to-text Generation with Variational Sequential Planningcited by 17
- 2022Recent Advances in Neural Text Generation: A Task-Agnostic Surveycited by 14
- 2024Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topicscited by 9
- 2024A Survey of Automatic Hallucination Evaluation on Natural Language Generationcited by 7
- 2024A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Modelscited by 6
- 2024Probing Omissions and Distortions in Transformer-based RDF-to-Text Modelscited by 0
- 2026Chatbots Output Meaningful (but Problematic) Languagecited by 0
- 2026Chatbots Output Meaningful (but Problematic) Language2 Jun 2026
- 2024Probing Omissions and Distortions in Transformer-based RDF-to-Text Models25 Sep 2024
- 2024A Survey of Automatic Hallucination Evaluation on Natural Language Generation18 Apr 2024
- 2024Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics13 Mar 2024
- 2024A Data-Centric Approach To Generate Faithful and High Quality Patient Summaries with Large Language Models23 Feb 2024
- 2023Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation1 May 2023
- 2022Recent Advances in Neural Text Generation: A Task-Agnostic Survey6 Mar 2022
- 2022Data-to-text Generation with Variational Sequential Planning28 Feb 2022
- 2022Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text14 Feb 2022