PHD POSITION - REPRESENTATION LEARNING FOR SIGN LANGUAGE TRANSLATION
USING LINGUISTIC AND KNOWLEDGE-BASED CONSTRAINTS
Within the context of the SignOn project funded by the European Horizon
2020 programme, the Centre for Computational Linguistics (CCL), part of
the ComForT research unit at KU Leuven, seeks to hire a PhD student to
carry out research on the subject of representation learning for sign
language translation.
Website unit [1]
PROJECT
The SignON project, which unites 17 European partners, aims to
facilitate the exchange of information among deaf, hard of hearing, and
hearing individuals across Europe by developing automatic sign language
translation tools. Automatic sign language translation (the task of
automatically translating a visual-gestural sign language utterance to
an oral language utterance and vice versa) is an application that has
the potential to reduce communicative barriers for millions of people.
The World Health Organisation reports that there are about 466 million
people in the world today with disabling hearing loss; and according to
the World Federation of the Deaf over 70 million people communicate
primarily via a sign language.
Sign languages are, just like verbal languages, highly structured
systems governed by a set of linguistic rules. There are, however, also
linguistic characteristics of signed languages that are modality
specific. As a consequence, sign language translation cannot be
considered as a one-to-one mapping from signs to spoken language words.
Recent machine learning methods have greatly improved the state-of-the
art in natural language processing applications, including the
multi-modal problem of sign language translation. However, due to the
inherent complexity of the task, most approaches do not favour an
end-to-end approach (i.e., directly translating sign to text), but first
transform the signs to an intermediate, gloss-based transcription (sign
to gloss), and in a second step translate the intermediate
representation to verbal language (gloss to text). Using glosses as an
interface for sign to language translation is fairly successful, but
also poses a number of problems. Gloss annotations are an imprecise
representation of sign language; in this respect, they are often an
impoverished representation that does not do justice to the complex
multi-channel production of sign language.
The PhD candidate will focus on the intermediate representation that
functions as an interface between sign language and verbal language in
the context of sign language translation. Research will be carried out
along two tracks:
* Firstly, the project will consider the development of a
multi-faceted interlingual representation for sign language translation,
that can function as a sufficiently rich interface between sign language
and verbal language, and is tailored towards machine learning methods.
Crucially, the representation needs to be sufficiently rich to capture
the intricacies of elaborate, multi-channel sign language, but at the
same time lenient enough to be incorporated into a classification-based
optimization objective that is inherent to machine learning approaches.
This task will be carried out in close cooperation with
linguistically-formed sign language experts; the representation will be
developed using Flemish Sign Language as a test-bed, but the resulting
representational framework should be generally applicable. Additionally,
the representational framework will be augmented with various
knowledge-based resources (such as WordNet and FrameNet) as well as
machine-learning based optimizations (i.e., informed by word and
sentence embeddings).
* Secondly, the project will examine how the resulting representations
can be exploited as soft constraints to improve the output predictions
of a neural machine translation architecture for sign language.
Specifically, the linguistic knowledge that is encoded within the
representation can be used to constrain the neural network's output
probability distribution. Learning-based approaches suffer from a lack
of resources: large-scale annotated sign language corpora are few and
far between. As a consequence, the resulting output predictions are
potentially syntactically unsound, semantically improbable, or otherwise
linguistically incongruous. By augmenting the network output with
representation-based constraints modeled as a priori distributions on
the neural network's output distribution, possible discrepancies can be
mediated. Additionally, the knowledge encoded in the representational
framework can be used to rerank the various candidates yielded by the
neural network architecture.
PROFILE
* You hold a Master in linguistics or computer science, or equivalent
education.
* You have solid programming skills.
* You exhibit excellent proficiency in English and good communication
skills.
* Working knowledge of Dutch is recommended; candidates without
knowledge of Dutch are welcome to apply if they are willing to learn
Dutch upon arrival.
* Experience with neural networks (deep learning) for natural language
processing is a plus.
* Knowledge of a sign language, particularly Flemish Sign Language, is
a valuable asset.
* Candidates who are deaf or hard of hearing are particularly
encouraged to apply.
OFFER
* We offer a fulltime PhD position for 1 year, extendable to 4 years
after initial positive evaluation.
* You will be able to conduct scientific research within a high-level
research environment, leading to a doctoral degree.
* You will work in a larger project, in cooperation with other Flemish
and European research groups.
* You will have the opportunity to participate in international
conferences, and benefit from academic training and workshops.
INTERESTED?
To apply, please send a motivation letter, a CV and the contact details
of two references with your application. For more information please
contact Tim Van de Cruys, mail: tim.vandecr...@kuleuven.be.
You can apply for this job no later than February 04, 2021 via the
online application tool [2]
KU Leuven seeks to foster an environment where all talents can flourish,
regardless of gender, age, cultural background, nationality or
impairments. If you have any questions relating to accessibility or
support, please contact us at diversiteit...@kuleuven.be.
Links:
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[1] https://www.arts.kuleuven.be/ling/comfort-english
[2] http://www.kuleuven.be/eapplyingforjobs/light/56123124
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