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Neural Text Generation from Structured Data with Application to the Biography Domain
Open accessAbstract
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.It generates biographical sentences from fact tables on a new dataset of biographies from Wikipedia.This set is an order of magnitude larger than existing resources with over 700k samples and a 400k vocabulary.Our model builds on conditional neural language models for text generation.To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that transfer sample-specific words from the input database to the generated output sentence.To deal with structured data, we allow the model to embed words differently depending on the data fields in which they occur.Our neural model significantly outperforms a Templated Kneser-Ney language model by nearly 15 BLEU.
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Cited by
Survey of Hallucination in Natural Language Generation
Ji, Z., Lee, N., Frieske, R., Yu, T., Su, D., Xu, Y., Ishii, E., Bang, Y. J., Madotto, A., & Fung, P. (2023) · ACM Computing Surveys · doi:10.1145/3571730 · Cited by 5,025
Parameter-efficient fine-tuning of large-scale pre-trained language models
Ding, N., Qin, Y., Yang, G., Wei, F., Yang, Z., Su, Y., Hu, S., Chen, Y., Chan, C.-M., Chen, W., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00626-4 · Cited by 1,191
On 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
Detecting hallucinations in large language models using semantic entropy
Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature · doi:10.1038/s41586-024-07421-0 · Cited by 934
“To generate the dataset, we randomly sampled 21 individuals from the WikiBio dataset 64 .”
Retrieval-Augmented Generation for Large Language Models: A Survey
Gao, Y., Xiong, Y., Gao, X., Jia, K., Pan, J., Bi, Y., Dai, Y., Sun, J., Wang, M., & Wang, H. (2023) · arXiv · doi:10.48550/arxiv.2312.10997 · Cited by 876
Metadata
- Authors
- Lebret, R., Grangier, D., & Auli, M.
- Venue
- Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Published
- 2016
- Pages
- 1203-1213
- OpenAlex
- W2523790121
- Cited by
- 444
- References
- 0
- FWCI
- 22.33
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Lebret, R., Grangier, D., & Auli, M. (2016). Neural Text Generation from Structured Data with Application to the Biography Domain. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.18653/v1/d16-1128
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
