Overview

Project: Hyperbolic Geometry for compositional generalisation in NLP

Keywords: NLP, compositional generalisation, hyperbolic geometry

We invite applications for an 18-month post-doc position at the Laboratoire
d’informatique de Grenoble (LIG, Université Grenoble Alpes).

Scientific context:

Despite their success in many NLP tasks, seq2seq models fail at learning
simple generalisation rules, i.e. interpreting novel combinations
of known lexical items, as illustrated by results on challenge datasets
such as SCAN [1] or COGS [2].
The HyperboTAL project is a multi-disciplinary collaboration
aiming at applying the tools of hyperbolic geometry to improve the
systematic compositional capabilities of Natural Language Processing models.
The project will build on recent proposals, as [3] and [4], that use
hyperpolic geometry to better encode linguistic information (e.g. Poincaré
embeddings).

Main tasks:

– Literature review on hyperbolic geometry applications in NLP and
deep learning approaches to systematic compositionality.
– Design compositionality models that bring together
the tools of hyperbolic geometry and those of deep learning.
– Implementation and evaluation of proposed models on existing
challenge datasets (COGS).

The scientific orientations of the post-doc may vary according to the
candidates’ background and interests.

Company:

Laboratoire d’Informatique de Grenoble

Qualifications:

– PhD in computer science.
– Background and/or strong interest in Mathematics and Natural Language
Processing.
– Programming skills: proficiency in python and experience with a deep
learning library.

Language requirements:

– Proficiency in either French or English.

Educational level:

Ph. D.

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