17 citations · 27 across the 9 of their papers we have counts for
10 papers
STRUCTURALCOST: A controlled reading time dataset for modeling human sentence processing difficulty
Nina Nusbaumer, Iria de-Dios-Flores, Corentin Bel +3
We introduce STRUCTURALCOST, a self-paced reading dataset of 475 participants and 40,800 observations isolating the processing cost of long-distance subject-verb dependency resolut…
From `Snippet-lects' to Doculects and Dialects: Leveraging Neural Representations of Speech for Placing Audio Signals in a Language Landscape
Séverine Guillaume, Guillaume Wisniewski, Alexis Michaud
XLSR-53 a multilingual model of speech, builds a vector representation from audio, which allows for a range of computational treatments. The experiments reported here use this neur…
ProsAudit, a prosodic benchmark for self-supervised speech models
Maureen de Seyssel, Marvin Lavechin, Hadrien Titeux +6
We present ProsAudit, a benchmark in English to assess structural prosodic knowledge in self-supervised learning (SSL) speech models. It consists of two subtasks, their correspondi…
Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement
Bingzhi Li, Guillaume Wisniewski, Benoît Crabbé
The long-distance agreement, evidence for syntactic structure, is increasingly used to assess the syntactic generalization of Neural Language Models. Much work has shown that trans…
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…
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…
Screening Gender Transfer in Neural Machine Translation
Guillaume Wisniewski, Lichao Zhu, Nicolas Ballier +1
This paper aims at identifying the information flow in state-of-the-art machine translation systems, taking as example the transfer of gender when translating from French into Engl…
Noisy UGC Translation at the Character Level: Revisiting Open-Vocabulary Capabilities and Robustness of Char-Based Models
José Carlos Rosales Núñez, Guillaume Wisniewski, Djamé Seddah
This work explores the capacities of character-based Neural Machine Translation to translate noisy User-Generated Content (UGC) with a strong focus on exploring the limits of such…
Understanding the Impact of UGC Specificities on Translation Quality
José Carlos Rosales Núñez, Djamé Seddah, Guillaume Wisniewski
This work takes a critical look at the evaluation of user-generated content automatic translation, the well-known specificities of which raise many challenges for MT. Our analyses…
Are Transformers a Modern Version of ELIZA? Observations on French Object Verb Agreement
Bingzhi Li, Guillaume Wisniewski, Benoit Crabbé
Many recent works have demonstrated that unsupervised sentence representations of neural networks encode syntactic information by observing that neural language models are able to…