mlsanityData to 7 Oct 2026

Generation Challenges: Results of the Accuracy Evaluation Shared Task

arXiv 2108.05644 ↗First posted 12 Aug 2021cs.CLcited by 9

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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

  1. 2017Challenges in Data-to-Document Generationcited by 226
  2. 2018Data-to-Text Generation with Content Selection and Planningcited by 106
  3. 2019Data-to-text Generation with Entity Modelingcited by 45
  4. 2020A Gold Standard Methodology for Evaluating Accuracy in Data-To-Text Systemscited by 34
  5. 2020Revisiting Challenges in Data-to-Text Generation with Fact Groundingcited by 27
  6. 2019A Hierarchical Model for Data-to-Text Generationcited by 20
  7. 2019Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)cited by 18
  8. 2019Learning to Select, Track, and Generate for Data-to-Textcited by 14
  9. 2020Scrutinizer: A Mixed-Initiative Approach to Large-Scale, Data-Driven Claim Verificationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 1; 2024: 4; 2025: 0; 2026: 1.

9 papers.