Pseudo-Semantic Graphs for Generating Paraphrases

• Type: Article in International Conference Proceedings Book
• Date: 2024

Authors

Sousa, A (Author) Other; Henrique Lopes Cardoso (Author) FEUP

Conference proceedings International

Title: Progress in Artificial Intelligence - 23rd EPIA Conference on Artificial Intelligence, EPIA 2024, Viana do Castelo, Portugal, September 3-6, 2024, Proceedings, Part III

Pages: 215-227

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

Viana do Castelo, 3 September 2024 through 6 September 2024

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Scopus - 0 Citations

Other information

Authenticus ID: P-017-CVA

DOI: 10.1007/978-3-031-73503-5_18

Abstract (EN): Paraphrases are texts written using different words but conveying the same meaning; hence, their quality is based upon retaining semantics while varying syntax/vocabulary. Recent works leverage structured syntactic information to control the syntax of the generations while relying on pretrained language models to retain the semantics. However, rarely do works in the literature consider using structured semantic information to enrich the language representation. In this work, we propose to model the task of paraphrase generation as a pseudo-Graph-to-Text task where we fine-tune pretrained language models using as input linearized representations of pseudo-semantic graphs built from dependency parsing trees sourced from the original input texts. Our model achieves competitive results on three popular paraphrase generation benchmarks. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12