6 citations · 11 across the 5 of their papers we have counts for
6 papers
Evaluating Document Simplification: On the Importance of Separately Assessing Simplicity and Meaning Preservation
Liam Cripwell, Joël Legrand, Claire Gardent
Text simplification intends to make a text easier to read while preserving its core meaning. Intuitively and as shown in previous works, these two dimensions (simplification and me…
Simplicity Level Estimate (SLE): A Learned Reference-Less Metric for Sentence Simplification
Liam Cripwell, Joël Legrand, Claire Gardent
Automatic evaluation for sentence simplification remains a challenging problem. Most popular evaluation metrics require multiple high-quality references -- something not readily av…
Context-Aware Document Simplification
Liam Cripwell, Joël Legrand, Claire Gardent
To date, most work on text simplification has focused on sentence-level inputs. Early attempts at document simplification merely applied these approaches iteratively over the sente…
Experiments on transfer learning architectures for biomedical relation extraction
Walid Hafiane, Joel Legrand, Yannick Toussaint +1
Relation extraction (RE) consists in identifying and structuring automatically relations of interest from texts. Recently, BERT improved the top performances for several NLP tasks,…
Neural Network-based Word Alignment through Score Aggregation
Joel Legrand, Michael Auli, Ronan Collobert
We present a simple neural network for word alignment that builds source and target word window representations to compute alignment scores for sentence pairs. To enable unsupervis…
Joint RNN-Based Greedy Parsing and Word Composition
Joël Legrand, Ronan Collobert
This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are j…