The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation
Open accessAbstract
Automatically constructed datasets for generating text from semi-structured data (tables), such as WikiBio (Lebret et al., 2016), often contain reference texts that diverge from the information in the corresponding semistructured data.We show that metrics which rely solely on the reference texts, such as BLEU and ROUGE, show poor correlation with human judgments when those references diverge.We propose a new metric, PAR-ENT, which aligns n-grams from the reference and generated texts to the semi-structured data before computing their precision and recall.Through a large scale human evaluation study of table-to-text models for WikiBio, we show that PARENT correlates with human judgments better than existing text generation metrics.We also adapt and evaluate the information extraction based evaluation proposed in Wiseman et al. (2017), and show that PAR-ENT has comparable correlation to it, while being easier to use.We show that PARENT is also applicable when the reference texts are elicited from humans using the data from the WebNLG challenge.1 * Work done during an internship at Google.
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Cites
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Survey of Hallucination in Natural Language Generation
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“PARENT [22] measures the accuracy of table-to-text generation by aligning n-grams from the reference description R and generated texts G to the table T .”
BackgroundMethodInfluentialOn Faithfulness and Factuality in Abstractive Summarization
Maynez, J., Narayan, S., Bohnet, B., & McDonald, R. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.173 · Cited by 1,185
What Makes Good In-Context Examples for GPT-3?
Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2022) · Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures · doi:10.18653/v1/2022.deelio-1.10 · Cited by 563
HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
Li, J., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.397 · Cited by 343
ToTTo: A Controlled Table-To-Text Generation Dataset
Parikh, A., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.89 · Cited by 252
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Metadata
- Authors
- Dhingra, B., Faruqui, M., Parikh, A., Chang, M.-W., Das, D., & Cohen, W.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
- Published
- 2019
- Pages
- 4884-4895
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2948628052
- Cited by
- 150
- References
- 40
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 13 · 2023: 33 · 2022: 26 · 2021: 35 · 2020: 17 · 2019: 1
- FWCI
- 7.56
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- table-to-text generation, evaluation metrics, semi-structured data, human evaluation correlation, WebNLG, BLEU, ROUGE
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