34.1205, Support: Computational Linguistics: PhD, Aix-Marseille University, France

[The LINGUIST List]

LINGUIST List: Vol-34-1205. Thu Apr 13 2023. ISSN: 1069 - 4875.

Subject: 34.1205, Support: Computational Linguistics: PhD, Aix-Marseille University, France

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Date: 13-Apr-2023
From: Philippe Blache [blache at ilcb.fr]
Subject: Computational Linguistics: PhD, Aix-Marseille University, France


Institution/Organization: Aix-Marseille University, France.
Department: LIS
Web Address: https://www.lis-lab.fr/

Level: PhD

Specialty Areas: Computational Linguistics

Description:

During conversation, many studies have shown that interlocutors tend
to progressively converge by adopting similar verbal and non-verbal
behaviors. We also know that the level of alignment between
participants can be correlated with the success of the interaction,
the quality of information transfer and inter-mutual comprehension.
The question is to know whether alignment is a consequence of our
perception of other's behavior, with a certain control and intention
in doing that, or whether it is a deeper spontaneous mechanism, based
on the fact that both participants form a unique system and behave
synchronously in an equal manner during the interaction. In the first
case, each participant has his/her own language model used in
production and perception and tries to decode the other production
from this model. In the second case, the hypothesis is that both
participants share the same model and generate the same predictions at
the same time, the speaker producing the message and the hearer
inhibiting it. This second options is based on the predictive coding
hypothesis, which can be observed at the brain level.

This PhD will address this question by investigating the different
hypothesis. In a first step, it will consist in evaluating during
conversation whether one unique model performs better than two
separate. These models will be built in two different manners: 1/ on
the basis of pre-trained models, fine-tuned thanks to existing
conversational datasets, 2/ with multimodal models, taking into
account all verbal and non-verbal features. A unique model based both
speakers production will be compared with separate model by
participant. In a second step, a model of alignment between
participants will be built, based on the different features extracted
from our datasets. Multimodal learning methods will be applied,
focusing in particular on the question of feature interaction and
modalities fusion. A correlation between the level of alignment and
the behavioral prediction will be  analyzed. In collaboration with
neuroscientists, we will finally analyze the dynamics in the brain
signal, looking for traces of synchronization predicted by our models.

Selection Criteria:
- A master’s degree in Computer Science, Artificial Intelligence,
Computational Linguistics, or a related field
- Knowledge of deep learning architectures, specifically Transformer
models
- Skills in natural language processing, multimodality and interaction
- Interest in interdisciplinary work
- Good research skills
- Good writing and presentation skills
- Good programming skills

Applications:
To apply for this vacancy, please send an email to blache at ilcb.fr.
Please ensure that you upload the following additional documents:
- Resume
- Application letter, including your applicability to aforementioned
selection criteria
- Grade list (preferably for both bachelor's degree and master's
degree)
- Examples of Scientific Writing (e.g., scientific publication or
master’s thesis)
- Reference letters

Application Deadline: 15-May-2023

Contact Information:
Philippe Blache
blache at ilcb.fr



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