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.- Anthology ID:
- 2021.eacl-main.180
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2100–2111
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.180
- DOI:
- 10.18653/v1/2021.eacl-main.180
- Cite (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.
- Cite (Informal):
- Neural-Driven Search-Based Paraphrase Generation (Fabre et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.eacl-main.180.pdf
- Data
- Opusparcus, PAWS