Neural-Driven Search-Based Paraphrase Generation

no code implementations • EACL 2021 • Betty Fabre, Tanguy Urvoy, Jonathan Chevelu, Damien Lolive

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.

Paraphrase Generation Sentence

Denoising Pre-Training and Data Augmentation Strategies for Enhanced RDF Verbalization with Transformers

no code implementations • ACL (WebNLG, INLG) 2020 • Sebastien Montella, Betty Fabre, Tanguy Urvoy, Johannes Heinecke, Lina Rojas-Barahona

The task of verbalization of RDF triples has known a growth in popularity due to the rising ubiquity of Knowledge Bases (KBs).

Data Augmentation Denoising +1

Neural-Driven Multi-criteria Tree Search for Paraphrase Generation

no code implementations • NeurIPS Workshop LMCA 2020 • Betty Fabre, Tanguy Urvoy, Jonathan Chevelu, Damien Lolive

A good paraphrase is semantically similar to the original sentence but it must be also well formed, and syntactically different to ensure diversity.

Paraphrase Generation Sentence