Evaluating Text GANs as Language Models
Open accessAbstract
Guy Tevet, Gavriel Habib, Vered Shwartz, Jonathan Berant. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
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GAN-generated text fails to outperform generations from language models when quality and diversity are considered jointly.
“but recent work has shown that when quality and diversity are considered jointly, GAN-generated text fails to outperform generations from language models (Caccia et al., 2018;Tevet et al., 2019;Semeniuta et al., 2018).”
From The Curious Case of Neural Text Degeneration (2019) · cites this paper as Tevet et al., 2019
The available indexed passages contain bibliographic records and an abstract, but no passage from the paper’s full text that can establish the claimed joint assessment of quality and diversity. Searches for that assessment and for evaluation terms returned no body-text passage, so the claim cannot be verified under the full-text-only evidence rule. (LLM, conf=0.72)
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Cites
Neural Network Methods for Natural Language Processing
Goldberg, Y. (2017) · Synthesis Lectures on Human Language Technologies · doi:10.1007/978-3-031-02165-7 · Cited by 821 · see citing works
Hierarchical Multiscale Recurrent Neural Networks
Chung, J., Ahn, S., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1609.01704 · Cited by 386 · see citing works
Flagged first, then by citations
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Sorted by citations
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844 · see citing works
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works
Evaluating the Evaluation of Diversity in Natural Language Generation
Tevet, G., & Berant, J. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.25 · Cited by 52 · see citing works
Self-citation
- Authors
- Tevet, G., Habib, G., Shwartz, V., & Berant, J.
- Venue
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Published
- 2019
- Pages
- 2241-2247
- OpenAlex
- W2963595537
- Cited by
- 12
- References
- 2
- Citations by year
- 2025: 1 · 2023: 2 · 2022: 1 · 2021: 3 · 2020: 4 · 2019: 1
- FWCI
- 0.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- text generation, generative adversarial networks, language models
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