The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer
Open accessAbstract
Juncen Li, Robin Jia, He He, Percy Liang. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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Cites
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.244 · Cited by 23,602
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965
ADADELTA: An Adaptive Learning Rate Method
Zeiler, M. D. (2012) · arXiv · doi:10.48550/arxiv.1212.5701 · Cited by 5,322
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Bousmalis, K., Silberman, N., Dohan, D., Erhan, D., & Krishnan, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.18 · Cited by 1,603
Generating Sentences by Editing Prototypes
Guu, K., Hashimoto, T. B., Oren, Y., & Liang, P. (2018) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00030 · Cited by 300 · cited 2×
“For D ELETE A ND R ETRIEVE , similar to Guu et al. (2018), we retrieve the top-10 sentences and generate results using markers from each sentence.”
BackgroundMethodSelf-citation
Cited by
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
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019) · arXiv · doi:10.48550/arxiv.1912.02164 · Cited by 508
CommonGen: A constrained text generation challenge for generative commonsense reasoning
Lin, B. Y., Zhou, W., Shen, M., Zhou, P., Bhagavatula, C., Choi, Y., & Ren, X. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.165 · Cited by 239
Self-Refine: Iterative Refinement with Self-Feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.17651 · Cited by 2,314
“While a large body of work on style transfer is directed at sentence-level sentiment transfer (Li et al., 2018; Prabhumoye et al., 2018), we focus on transferring the sentiment of entire reviews, making the task challenging and providing opportunities for iterative improvements.”
BackgroundUnifying Human and Statistical Evaluation for Natural Language Generation
Hashimoto, T. B., Zhang, H., & Liang, P. (2019) · 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) · doi:10.18653/v1/n19-1169 · Cited by 161
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Metadata
- Authors
- Li, J., Jia, R., He, H., & Liang, P.
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1865-1874
- OpenAlex
- W2963667126
- Cited by
- 468
- References
- 5
- FWCI
- 29.44
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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- Li, J., Jia, R., He, H., & Liang, P. (2018). Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). https://doi.org/10.18653/v1/n18-1169
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
