@inproceedings{peng-etal-2019-text,
title = "Text Generation with Exemplar-based Adaptive Decoding",
author = "Peng, Hao and
Parikh, Ankur and
Faruqui, Manaal and
Dhingra, Bhuwan and
Das, Dipanjan",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1263",
doi = "10.18653/v1/N19-1263",
pages = "2555--2565",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Text Generation with Exemplar-based Adaptive Decoding
%A Peng, Hao
%A Parikh, Ankur
%A Faruqui, Manaal
%A Dhingra, Bhuwan
%A Das, Dipanjan
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F peng-etal-2019-text
%X 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.
%R 10.18653/v1/N19-1263
%U https://aclanthology.org/N19-1263
%U https://doi.org/10.18653/v1/N19-1263
%P 2555-2565
Markdown (Informal)
[Text Generation with Exemplar-based Adaptive Decoding](https://aclanthology.org/N19-1263) (Peng et al., NAACL 2019)
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.