activity
20192023
most citedA Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

6 citations · 11 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2023★ 4 cited

LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development

Ilias Chalkidis, Nicolas Garneau, Catalina Goanta +2

In this work, we conduct a detailed analysis on the performance of legal-oriented pre-trained language models (PLMs). We examine the interplay between their original objective, acq…

cs.CL2020

Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations

David Beauchemin, Nicolas Garneau, Eve Gaumond +3

Plumitifs (dockets) were initially a tool for law clerks. Nowadays, they are used as summaries presenting all the steps of a judicial case. Information concerning parties' identity…

cs.LG2019★ 1 cited

Attending Form and Context to Generate Specialized Out-of-VocabularyWords Representations

Nicolas Garneau, Jean-Samuel Leboeuf, Yuval Pinter +1

We propose a new contextual-compositional neural network layer that handles out-of-vocabulary (OOV) words in natural language processing (NLP) tagging tasks. This layer consists of…

cs.LG2019★ 6 cited

A Robust Self-Learning Method for Fully Unsupervised Cross-Lingual Mappings of Word Embeddings: Making the Method Robustly Reproducible as Well

Nicolas Garneau, Mathieu Godbout, David Beauchemin +2

In this paper, we reproduce the experiments of Artetxe et al. (2018b) regarding the robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings. We…

cs.CL2019

Predicting and interpreting embeddings for out of vocabulary words in downstream tasks

Nicolas Garneau, Jean-Samuel Leboeuf, Luc Lamontagne

We propose a novel way to handle out of vocabulary (OOV) words in downstream natural language processing (NLP) tasks. We implement a network that predicts useful embeddings for OOV…