Connectionists: Internship position on Reinforcement Learning for Controlled Text Generation , NAVER Labs Europe (Grenoble, France)

[Marc Dymetman]

Are you a currently enrolled student at the Master or PhD level

with a strong interest in Deep Learning, Reinforcement Learning,

and/or NLP?

 

NAVER Labs Europe is opening an internship position on Reinforcement Learning

for Controlled Text Generation, which might be of interest to you. See

below.

 

For more information and for applying, please visit the page

https://europe.naverlabs.com/job/reinforcement-learning-for-controlled-text-generation/

and follow the instructions given there. 

 

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NAVER LABS Europe: Internship  

 

Start date: Fall 2019 or early 2020

Duration; 5-6 months

 

We are opening a research internship on Reinforcement

Learning techniques for applications to controlled text generation.

Under conditions where training data is limited, standard end-to-end

training of seq2seq models may generalize poorly and produce

inadequate results at test time. A possible remedy is to augment

models with rewards that control the quality of the outputs. These

rewards can address two complementary goals: (i) taking into account

global characteristics of observed sequences that go beyond standard

local teacher-forcing training techniques (observation bias problem),

and (ii) moving the generation process towards desired properties of

the output (e.g. favoring shorter sentences or performing style

transfer).

 

Supervisors: Marc Dymetman and Hady Elsahar.

 

We are looking for a motivated intern to help us develop methods and

algorithms for addressing this general problem, both in theory and in

practice. Experiments will be conducted on selected text generation

tasks (NLG, summarization or machine translation).

 

The successful candidate should be enrolled in a graduate program, at

the Master or (preferably) PhD level, with experience (ideally) in

Deep Learning, Reinforcement Learning and Natural Language Processing.

 

Publication of results in major conferences/journals will be strongly

encouraged.

 

REQUIRED SKILLS : Strong mathematical and programming skills as well

as familiarity with one of the major current deep learning toolkits

(PyTorch preferred but not compulsory) are a requirement.

 

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