Text Generation with Exemplar-based Adaptive Decoding
Hao Peng, Ankur Parikh, Manaal Faruqui, Bhuwan Dhingra, Dipanjan Das
Abstract
We propose a novel conditioned text generation model. It draws inspiration from traditional template-based text generation techniques, where the source provides the content (i.e., what to say), and the template influences how to say it. Building on the successful encoder-decoder paradigm, it first encodes the content representation from the given input text; to produce the output, it retrieves exemplar text from the training data as “soft templates,” which are then used to construct an exemplar-specific decoder. We evaluate the proposed model on abstractive text summarization and data-to-text generation. Empirical results show that this model achieves strong performance and outperforms comparable baselines.- Anthology ID:
- N19-1263
- Volume:
- 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)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2555–2565
- Language:
- URL:
- https://aclanthology.org/N19-1263
- DOI:
- 10.18653/v1/N19-1263
- Cite (ACL):
- Hao Peng, Ankur Parikh, Manaal Faruqui, Bhuwan Dhingra, and Dipanjan Das. 2019. Text Generation with Exemplar-based Adaptive Decoding. In 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), pages 2555–2565, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Text Generation with Exemplar-based Adaptive Decoding (Peng et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/N19-1263.pdf
- Data
- New York Times Annotated Corpus, WikiBio