Murielle Fabre


Modeling conventionalization and predictability within MWEs at the brain level
Shohini Bhattasali | Murielle Fabre | Christophe Pallier | John Hale
Proceedings of the Society for Computation in Linguistics 2020


Variable beam search for generative neural parsing and its relevance for the analysis of neuro-imaging signal
Benoit Crabbé | Murielle Fabre | Christophe Pallier
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

This paper describes a method of variable beam size inference for Recurrent Neural Network Grammar (rnng) by drawing inspiration from sequential Monte-Carlo methods such as particle filtering. The paper studies the relevance of such methods for speeding up the computations of direct generative parsing for rnng. But it also studies the potential cognitive interpretation of the underlying representations built by the search method (beam activity) through analysis of neuro-imaging signal.


Modeling Brain Activity Associated with Pronoun Resolution in English and Chinese
Jixing Li | Murielle Fabre | Wen-Ming Luh | John Hale
Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference

Typological differences between English and Chinese suggest stronger reliance on salience of the antecedent during pronoun resolution in Chinese. We examined this hypothesis by correlating a difficulty measure of pronoun resolution derived by the activation-based ACT-R model with the brain activity of English and Chinese participants listening to a same audiobook during fMRI recording. The ACT-R model predicts higher overall difficulty for English speakers, which is supported at the brain level in left Broca’s area. More generally, it confirms that computational modeling approach is able to dissociate different dimensions that are involved in the complex process of pronoun resolution in the brain.

The Role of Syntax During Pronoun Resolution: Evidence from fMRI
Jixing Li | Murielle Fabre | Wen-Ming Luh | John Hale
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

The current study examined the role of syntactic structure during pronoun resolution. We correlated complexity measures derived by the syntax-sensitive Hobbs algorithm and a neural network model for pronoun resolution with brain activity of participants listening to an audiobook during fMRI recording. Compared to the neural network model, the Hobbs algorithm is associated with larger clusters of brain activation in a network including the left Broca’s area.

Processing MWEs: Neurocognitive Bases of Verbal MWEs and Lexical Cohesiveness within MWEs
Shohini Bhattasali | Murielle Fabre | John Hale
Proceedings of the Joint Workshop on Linguistic Annotation, Multiword Expressions and Constructions (LAW-MWE-CxG-2018)

Multiword expressions have posed a challenge in the past for computational linguistics since they comprise a heterogeneous family of word clusters and are difficult to detect in natural language data. In this paper, we present a fMRI study based on language comprehension to provide neuroimaging evidence for processing MWEs. We investigate whether different MWEs have distinct neural bases, e.g. if verbal MWEs involve separate brain areas from non-verbal MWEs and if MWEs with varying levels of cohesiveness activate dissociable brain regions. Our study contributes neuroimaging evidence illustrating that different MWEs elicit spatially distinct patterns of activation. We also adapt an association measure, usually used to detect MWEs, as a cognitively plausible metric for language processing.