@inproceedings{fabre-etal-2021-neural,
title = "Neural-Driven Search-Based Paraphrase Generation",
author = "Fabre, Betty and
Urvoy, Tanguy and
Chevelu, Jonathan and
Lolive, Damien",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.180",
doi = "10.18653/v1/2021.eacl-main.180",
pages = "2100--2111",
abstract = "We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.",
}
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<abstract>We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.</abstract>
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%0 Conference Proceedings
%T Neural-Driven Search-Based Paraphrase Generation
%A Fabre, Betty
%A Urvoy, Tanguy
%A Chevelu, Jonathan
%A Lolive, Damien
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F fabre-etal-2021-neural
%X We study a search-based paraphrase generation scheme where candidate paraphrases are generated by iterated transformations from the original sentence and evaluated in terms of syntax quality, semantic distance, and lexical distance. The semantic distance is derived from BERT, and the lexical quality is based on GPT2 perplexity. To solve this multi-objective search problem, we propose two algorithms: Monte-Carlo Tree Search For Paraphrase Generation (MCPG) and Pareto Tree Search (PTS). We provide an extensive set of experiments on 5 datasets with a rigorous reproduction and validation for several state-of-the-art paraphrase generation algorithms. These experiments show that, although being non explicitly supervised, our algorithms perform well against these baselines.
%R 10.18653/v1/2021.eacl-main.180
%U https://aclanthology.org/2021.eacl-main.180
%U https://doi.org/10.18653/v1/2021.eacl-main.180
%P 2100-2111
Markdown (Informal)
[Neural-Driven Search-Based Paraphrase Generation](https://aclanthology.org/2021.eacl-main.180) (Fabre et al., EACL 2021)
ACL
- Betty Fabre, Tanguy Urvoy, Jonathan Chevelu, and Damien Lolive. 2021. Neural-Driven Search-Based Paraphrase Generation. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2100–2111, Online. Association for Computational Linguistics.