61 citations · 72 across the 14 of their papers we have counts for
17 papers
Pantagruel: Unified Self-Supervised Encoders for French Text and Speech
Phuong-Hang Le, Valentin Pelloin, Arnault Chatelain +27
We release Pantagruel models, a new family of self-supervised encoder models for French text and speech. Instead of predicting modality-tailored targets such as textual tokens or s…
ding-01 :ARG0: An AMR Corpus for Spontaneous French Dialogue
Jeongwoo Kang, Maria Boritchev, Maximin Coavoux
We present our work to build a French semantic corpus by annotating French dialogue in Abstract Meaning Representation (AMR). Specifically, we annotate the DinG corpus, consisting…
Reassessing Graph Linearization for Sequence-to-sequence AMR Parsing: On the Advantages and Limitations of Triple-Based Encoding
Jeongwoo Kang, Maximin Coavoux, Cédric Lopez +1
Sequence-to-sequence models are widely used to train Abstract Meaning Representation (Banarescu et al., 2013, AMR) parsers. To train such models, AMR graphs have to be linearized i…
Should Cross-Lingual AMR Parsing go Meta? An Empirical Assessment of Meta-Learning and Joint Learning AMR Parsing
Jeongwoo Kang, Maximin Coavoux, Cédric Lopez +1
Cross-lingual AMR parsing is the task of predicting AMR graphs in a target language when training data is available only in a source language. Due to the small size of AMR training…
Growing Trees on Sounds: Assessing Strategies for End-to-End Dependency Parsing of Speech
Adrien Pupier, Maximin Coavoux, Jérôme Goulian +1
Direct dependency parsing of the speech signal -- as opposed to parsing speech transcriptions -- has recently been proposed as a task (Pupier et al. 2022), as a way of incorporatin…
What has LeBenchmark Learnt about French Syntax?
Zdravko Dugonjić, Adrien Pupier, Benjamin Lecouteux +1
The paper reports on a series of experiments aiming at probing LeBenchmark, a pretrained acoustic model trained on 7k hours of spoken French, for syntactic information. Pretrained…
LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech
Titouan Parcollet, Ha Nguyen, Solene Evain +19
Self-supervised learning (SSL) is at the origin of unprecedented improvements in many different domains including computer vision and natural language processing. Speech processing…
BERT is not The Count: Learning to Match Mathematical Statements with Proofs
Weixian Waylon Li, Yftah Ziser, Maximin Coavoux +1
We introduce a task consisting in matching a proof to a given mathematical statement. The task fits well within current research on Mathematical Information Retrieval and, more gen…
On Detecting Policy-Related Political Ads: An Exploratory Analysis of Meta Ads in 2022 French Election
Vera Sosnovik, Romaissa Kessi, Maximin Coavoux +1
Online political advertising has become the cornerstone of political campaigns. The budget spent solely on political advertising in the U.S. has increased by more than 100% from $…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Learning to Match Mathematical Statements with Proofs
Maximin Coavoux, Shay B. Cohen
We introduce a novel task consisting in assigning a proof to a given mathematical statement. The task is designed to improve the processing of research-level mathematical texts. Ap…
Self-Supervised and Controlled Multi-Document Opinion Summarization
Hady Elsahar, Maximin Coavoux, Matthias Gallé +1
We address the problem of unsupervised abstractive summarization of collections of user generated reviews with self-supervision and control. We propose a self-supervised setup that…
FlauBERT: Unsupervised Language Model Pre-training for French
Hang Le, Loïc Vial, Jibril Frej +7
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts…
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle
Maximin Coavoux, Shay B. Cohen
We introduce a novel transition system for discontinuous constituency parsing. Instead of storing subtrees in a stack --i.e. a data structure with linear-time sequential access-- t…
Unlexicalized Transition-based Discontinuous Constituency Parsing
Maximin Coavoux, Benoît Crabbé, Shay B. Cohen
Lexicalized parsing models are based on the assumptions that (i) constituents are organized around a lexical head (ii) bilexical statistics are crucial to solve ambiguities. In thi…
Privacy-preserving Neural Representations of Text
Maximin Coavoux, Shashi Narayan, Shay B. Cohen
This article deals with adversarial attacks towards deep learning systems for Natural Language Processing (NLP), in the context of privacy protection. We study a specific type of a…
Cross-lingual RST Discourse Parsing
Chloé Braud, Maximin Coavoux, Anders Søgaard
Discourse parsing is an integral part of understanding information flow and argumentative structure in documents. Most previous research has focused on inducing and evaluating mode…