no code implementations • FNP (LREC) 2022 • Negar Foroutan, Angelika Romanou, Stéphane Massonnet, Rémi Lebret, Karl Aberer
The language models were fine-tuned on a financial document collection of three languages (English, Spanish, and Greek) and aim to identify the beginning of the summary narrative part of the document.
1 code implementation • 8 Feb 2023 • Mohammadreza Banaei, Klaudia Bałazy, Artur Kasymov, Rémi Lebret, Jacek Tabor, Karl Aberer
Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks.
no code implementations • 15 Nov 2022 • Sepideh Mamooler, Rémi Lebret, Stéphane Massonnet, Karl Aberer
However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire.
1 code implementation • 30 Mar 2022 • Tim Poštuvan, Jiaxuan You, Mohammadreza Banaei, Rémi Lebret, Jure Leskovec
To mitigate these limitations, we propose Adaptive Grid Search (AdaGrid), which dynamically adjusts the edge message ratio during training.
no code implementations • 14 Sep 2021 • Saibo Geng, Rémi Lebret, Karl Aberer
This work investigates the value of domain adaptive pre-training and language adapters in legal NLP tasks.
1 code implementation • ACL (RepL4NLP) 2021 • Klaudia Bałazy, Mohammadreza Banaei, Rémi Lebret, Jacek Tabor, Karl Aberer
The adoption of Transformer-based models in natural language processing (NLP) has led to great success using a massive number of parameters.
no code implementations • 5 Jun 2020 • Mohammadreza Banaei, Rémi Lebret, Karl Aberer
This paper presents our approach for SwissText & KONVENS 2020 shared task 2, which is a multi-stage neural model for Swiss German (GSW) identification on Twitter.
no code implementations • 23 Apr 2020 • Fangyu Liu, Rémi Lebret, Didier Orel, Philippe Sordet, Karl Aberer
The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs.
no code implementations • 31 Dec 2018 • Fábio Perez, Rémi Lebret, Karl Aberer
In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks.
2 code implementations • 7 Feb 2018 • Hamza Harkous, Kassem Fawaz, Rémi Lebret, Florian Schaub, Kang G. Shin, Karl Aberer
Companies, users, researchers, and regulators still lack usable and scalable tools to cope with the breadth and depth of privacy policies.
no code implementations • 25 Apr 2017 • Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer
We propose a simple, yet effective, approach towards inducing multilingual taxonomies from Wikipedia.
no code implementations • 25 Apr 2017 • Amit Gupta, Rémi Lebret, Hamza Harkous, Karl Aberer
We propose a novel, semi-supervised approach towards domain taxonomy induction from an input vocabulary of seed terms.
no code implementations • 18 Jun 2015 • Rémi Lebret, Ronan Collobert
We evaluate the quality of the word representations on several classical word evaluation tasks, and we introduce a novel task to evaluate the quality of the phrase representations.
no code implementations • 12 Feb 2015 • Rémi Lebret, Pedro O. Pinheiro, Ronan Collobert
Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing.
no code implementations • 19 Dec 2014 • Rémi Lebret, Ronan Collobert
The number of features is therefore dramatically reduced and documents can be represented as bag of semantic concepts.
no code implementations • 16 Dec 2014 • Rémi Lebret, Ronan Collobert
We present a systematic study of the use of the Hellinger distance to extract semantic representations from the word co-occurence statistics of large text corpora.
no code implementations • 19 Dec 2013 • Rémi Lebret, Ronan Collobert
Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks.