@inproceedings{yu-etal-2020-delexicalized,
title = "Delexicalized Paraphrase Generation",
author = "Yu, Boya and
Arkoudas, Konstantine and
Hamza, Wael",
editor = "Clifton, Ann and
Napoles, Courtney",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics: Industry Track",
month = dec,
year = "2020",
address = "Online",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-industry.10",
doi = "10.18653/v1/2020.coling-industry.10",
pages = "102--112",
abstract = "We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots, we apply convolutional neural networks (CNN) prior to pooling on slot values and use pointers to locate slots in the output. We show empirically that the generated paraphrases are of high quality, leading to an additional 1.29{\%} exact match on live utterances. We also show that natural language understanding (NLU) tasks, such as intent classification and named entity recognition, can benefit from data augmentation using automatically generated paraphrases.",
}
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%0 Conference Proceedings
%T Delexicalized Paraphrase Generation
%A Yu, Boya
%A Arkoudas, Konstantine
%A Hamza, Wael
%Y Clifton, Ann
%Y Napoles, Courtney
%S Proceedings of the 28th International Conference on Computational Linguistics: Industry Track
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Online
%F yu-etal-2020-delexicalized
%X We present a neural model for paraphrasing and train it to generate delexicalized sentences. We achieve this by creating training data in which each input is paired with a number of reference paraphrases. These sets of reference paraphrases represent a weak type of semantic equivalence based on annotated slots and intents. To understand semantics from different types of slots, other than anonymizing slots, we apply convolutional neural networks (CNN) prior to pooling on slot values and use pointers to locate slots in the output. We show empirically that the generated paraphrases are of high quality, leading to an additional 1.29% exact match on live utterances. We also show that natural language understanding (NLU) tasks, such as intent classification and named entity recognition, can benefit from data augmentation using automatically generated paraphrases.
%R 10.18653/v1/2020.coling-industry.10
%U https://aclanthology.org/2020.coling-industry.10
%U https://doi.org/10.18653/v1/2020.coling-industry.10
%P 102-112
Markdown (Informal)
[Delexicalized Paraphrase Generation](https://aclanthology.org/2020.coling-industry.10) (Yu et al., COLING 2020)
ACL
- Boya Yu, Konstantine Arkoudas, and Wael Hamza. 2020. Delexicalized Paraphrase Generation. In Proceedings of the 28th International Conference on Computational Linguistics: Industry Track, pages 102–112, Online. International Committee on Computational Linguistics.