Are you a currently enrolled student at the PhD or Master 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. ----------- 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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