461 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Centre National de la Recherche ScientifiqueFR6 papers
- École des hautes études en sciences socialesFR6 papers
- École Normale Supérieure - PSLFR6 papers
- Laboratoire de Linguistique FormelleFR5 papers
- Université Paris Sciences et LettresFR5 papers
- Laboratoire d'Informatique de GrenobleFR4 papers
- Meta (Israel)IL4 papers
- University of TorontoCA4 papers
- Brno University of TechnologyCZ3 papers
- Meta (United States)US3 papers
- Canada Research ChairsCA2 papers
- Carnegie Mellon UniversityUS2 papers
29 papers
Biasless Language Models Learn Unnaturally: How LLMs Fail to Distinguish the Possible from the Impossible
Imry Ziv, Nur Lan, Emmanuel Chemla
Are large language models (LLMs) sensitive to the distinction between humanly possible and impossible languages? This question was recently used in a broader debate on whether LLMs…
A Minimum Description Length Approach to Regularization in Neural Networks
Matan Abudy, Orr Well, Emmanuel Chemla +2
State-of-the-art neural networks can be trained to become remarkable solutions to many problems. But while these architectures can express symbolic, perfect solutions, trained mode…
The Impact of Syntactic and Semantic Proximity on Machine Translation with Back-Translation
Nicolas Guerin, Shane Steinert-Threlkeld, Emmanuel Chemla
Unsupervised on-the-fly back-translation, in conjunction with multilingual pretraining, is the dominant method for unsupervised neural machine translation. Theoretically, however,…
Adaptation of the Multi-Concept Multivariate Elo Rating System to Medical Students Training Data
Erva Nihan Kandemir, Jill-Jenn Vie, Adam Sanchez-Ayte +2
Accurate estimation of question difficulty and prediction of student performance play key roles in optimizing educational instruction and enhancing learning outcomes within digital…
Metric Learning Encoding Models: A Multivariate Framework for Interpreting Neural Representations
Louis Jalouzot, Christophe Pallier, Emmanuel Chemla +1
Understanding how explicit theoretical features are encoded in opaque neural systems is a central challenge now common to neuroscience and AI. We introduce Metric Learning Encoding…
On three-valued presentations of classical logic
Bruno da Ré, Damian Szmuc, Emmanuel Chemla +1
Given a three-valued definition of validity, which choice of three-valued truth tables for the connectives can ensure that the resulting logic coincides exactly with classical logi…
Minimum Description Length Hopfield Networks
Matan Abudy, Nur Lan, Emmanuel Chemla +1
Associative memory architectures are designed for memorization but also offer, through their retrieval method, a form of generalization to unseen inputs: stored memories can be see…
Benchmarking Neural Network Generalization for Grammar Induction
Nur Lan, Emmanuel Chemla, Roni Katzir
How well do neural networks generalize? Even for grammar induction tasks, where the target generalization is fully known, previous works have left the question open, testing very l…
BabySLM: language-acquisition-friendly benchmark of self-supervised spoken language models
Marvin Lavechin, Yaya Sy, Hadrien Titeux +5
Self-supervised techniques for learning speech representations have been shown to develop linguistic competence from exposure to speech without the need for human labels. In order…
Are word boundaries useful for unsupervised language learning?
Tu Anh Nguyen, Maureen de Seyssel, Robin Algayres +3
Word or word-fragment based Language Models (LM) are typically preferred over character-based ones in many downstream applications. This may not be surprising as words seem more li…
Is the Language Familiarity Effect gradual? A computational modelling approach
Maureen de Seyssel, Guillaume Wisniewski, Emmanuel Dupoux
According to the Language Familiarity Effect (LFE), people are better at discriminating between speakers of their native language. Although this cognitive effect was largely studie…
Toward a realistic model of speech processing in the brain with self-supervised learning
Juliette Millet, Charlotte Caucheteux, Pierre Orhan +5
Several deep neural networks have recently been shown to generate activations similar to those of the brain in response to the same input. These algorithms, however, remain largely…
Probing phoneme, language and speaker information in unsupervised speech representations
Maureen de Seyssel, Marvin Lavechin, Yossi Adi +2
Unsupervised models of representations based on Contrastive Predictive Coding (CPC)[1] are primarily used in spoken language modelling in that they encode phonetic information. In…
Generative Spoken Dialogue Language Modeling
Tu Anh Nguyen, Eugene Kharitonov, Jade Copet +8
We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled wi…
Speech Resynthesis from Discrete Disentangled Self-Supervised Representations
Adam Polyak, Yossi Adi, Jade Copet +5
We propose using self-supervised discrete representations for the task of speech resynthesis. To generate disentangled representation, we separately extract low-bitrate representat…
The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling
Tu Anh Nguyen, Maureen de Seyssel, Patricia Rozé +5
We introduce a new unsupervised task, spoken language modeling: the learning of linguistic representations from raw audio signals without any labels, along with the Zero Resource S…
The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units
Ewan Dunbar, Julien Karadayi, Mathieu Bernard +6
We present the Zero Resource Speech Challenge 2020, which aims at learning speech representations from raw audio signals without any labels. It combines the data sets and metrics f…
Vocal markers from sustained phonation in Huntington's Disease
Rachid Riad, Hadrien Titeux, Laurie Lemoine +5
Disease-modifying treatments are currently assessed in neurodegenerative diseases. Huntington's Disease represents a unique opportunity to design automatic sub-clinical markers, ev…
Training design fostering the emergence of new meanings toward unprecedented and critical events
Simon Flandin, Deli Salini, Artémis Drakos +1
Our research is part of a technological research program in adult education conducted in reference to the "course of action" program. Using the activity-sign hypothesis of this pro…
An open-source voice type classifier for child-centered daylong recordings
Marvin Lavechin, Ruben Bousbib, Hervé Bredin +2
Spontaneous conversations in real-world settings such as those found in child-centered recordings have been shown to be amongst the most challenging audio files to process. Neverth…
Identification of primary and collateral tracks in stuttered speech
Rachid Riad, Anne-Catherine Bachoud-Lévi, Frank Rudzicz +1
Disfluent speech has been previously addressed from two main perspectives: the clinical perspective focusing on diagnostic, and the Natural Language Processing (NLP) perspective ai…
Seshat: A tool for managing and verifying annotation campaigns of audio data
Hadrien Titeux, Rachid Riad, Xuan-Nga Cao +5
We introduce Seshat, a new, simple and open-source software to efficiently manage annotations of speech corpora. The Seshat software allows users to easily customise and manage ann…
Libri-Light: A Benchmark for ASR with Limited or No Supervision
Jacob Kahn, Morgane Rivière, Weiyi Zheng +12
We introduce a new collection of spoken English audio suitable for training speech recognition systems under limited or no supervision. It is derived from open-source audio books f…
Neural language modeling of free word order argument structure
Charlotte Rochereau, Benoît Sagot, Emmanuel Dupoux
Neural language models trained with a predictive or masked objective have proven successful at capturing short and long distance syntactic dependencies. Here, we focus on verb argu…