> > > PhD thesis offer in France/ Learning from Post-Edition in Machine > Translation / LIFL (Lille) and LIG (Grenoble) > > > Contacts : Olivier Pietquin : olivier.pietq...@univ-lille1.fr Laurent > Besacier : laurent.besac...@imag.fr > > > Problem > > Statistical Machine Translation (SMT) is the process by which texts are > automatically translated from a source language to a target language by > a machine that has been trained on corpora in both languages. Thanks to > progress in the training of SMT engines, machine translation has become > good enough so that it has become advantageous for translators to > post-edit machine outputs rather than translate from scratch. However, > current enhancement of SMT systems from human post-edition (PE) are > rather basic: the post-edited output is added to the training corpus and > the translation model and language model are re-trained, with no clear > view of how much has been improved and how much is left to be > improved. Moreover, the final PE result is the only feedback used: > available technologies do not take advantage of logged sequences of > post-edition actions, which inform on the cognitive processes of the > post-editor. > > The proposed thesis aims at using the post-edition process as a > demonstration of how an expert translator modifies the SMT result to > produce a perfect translation. Learning from demonstration is an > emerging field in machine learning, mostly applied to robotics [1] that > will thus be explored further in the particular framework of SMT. > > Topic of research > > A novel approach to SMT training will be adopted in this thesis, i.e. > considering the post-edition process as a sequential decision making > process performed by human experts who should be imitated. This thesis’ > first fundamental contribution to SMT will be to reformulate the problem > of post-edition in SMT as a sequential decision making problem > [4]. Indeed, the hypothesis selection and ranking process occurring in > an SMT system can be seen as an action selection strategy, choosing > after each post-edition step amongst a large number of actions (all > possible hypotheses and rankings). This strategy has to be modified > according to post-edition results arising sequentially and being > influenced by previous actions (hypothesis selection) of the system. > > From this, SMT will be casted into an imitation learning problem, that > is learning from demonstrations made by an expert: post-edition results > can be seen as examples of what the system should do, again in a > sequential decision making process and not in a static one such as > supervised learning. Indeed, SMT decoding, whether it is based on > phrases or chunks, can be seen as a sequential decision making > process. The sequences of decisions taken by an expert during the > post-edition process can be seen as a target for the system, which will > try to imitate them in similar situations. To do so, we will extend the > work described in [2], that modelled semantic parsing as an Inverse > Reinforcement Learning (IRL) [3]. > > In addition, the question of automatically selecting the sentences that > should be used for post-edition and further learning will be addressed. > Especially, this will be studied under the active learning > paradigm. Large and diversified amounts of post-edited data, collected > in an industrial setting, will be made available for the research > project. > > > Profile > > The applicants must hold an Engineering or a Master degree in > Computational Linguistics or computer science, preferably with > experience in the fields of statistical machine learning and/or natural > language processing. Good background in programming will also be > required. He/she will also be involved in a research project, funded by > the French National Agency for Research, involving 2 research labs (LIFL > in Lille and LIG in Grenoble) and a company (Lingua & Machina). For this > reason good English level is required (good command of French being a > plus). Finally effective communication skills in English, both written > and verbal are mandatory. > > Context > > The candidate will be hired by University Lille 1 in the framework of a > national research project. S/he will mainly be hosted in the SequeL ( > Sequential Learning) team of the Laboratoire d’Informatique Fondamentale > de Lille (LIFL). SequeL is also a common team-project with INRIA > (national institute for research in computer science and mathematics) > and espe- cially the INRIA Lille - Nord Europe Center. The group > involves around 25 researchers working on sequential learning and is > internationally recognized. Lille is the largest city of the north of > France, a metropolis with 1 million inhabitants, with excellent train > connections to Brussels (30 min), Paris (1h) and London (1h30). > > This thesis will be supervised in strong collaboration with the GETALP > team of Laboratoire d’Informatique de Grenoble (LIG), widely renowned > for its research on natural language and speech processing. Grenoble is > a high-tech city with 4 universities. It is located at the heart of the > Alps, in outstanding scientific and natural surroundings. It is 3h by > train from Paris ; 2h from Geneva ; 1h from Lyon ; 2h from Torino and is > less than 1h from Lyon international airport. > > The PhD thesis will be co-supervised by Olivier Pietquin in Lille and > Laurent Besacier in Grenoble. > > Contacts > > Interviews will be held in Sept 2014. Meetings during Interspeech 2014 > in Singapore can be also organized. For further info, please contact: > > Olivier Pietquin : olivier.pietq...@univ-lille1.fr > Laurent Besacier : laurent.besac...@imag.fr > > References > > [1] Brenna D. Argall, Sonia Chernova, Manuela Veloso, and Brett > Browning. A survey of robot learning from demonstration. Robotics and > Autonomous Systems, 57(5):469–483, May 2009. > > [2] Gergely Neu and Csaba SzepesvÃąri. Training parsers by inverse > reinforcement learning. Machine Learning, 77(2-3):303–337, 2009. > > [3] Andrew Y. Ng and Stuart J. Russell. Algorithms for inverse > reinforcement learning. In Proceedings of the Seventeenth International > Conference on Machine Learning, ICML ’00, pages 663–670, San Francisco, > CA, USA, 2000. Morgan Kaufmann Publishers Inc. > > [4] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An > Introduction. The MIT Press, 3rd edition, March 1998. > > > > >
------------------------ Laurent Besacier Professeur à l'Université Joseph Fourier (Grenoble 1) Laboratoire d'Informatique de Grenoble (LIG) Membre Junior de l'Institut Universitaire de France (IUF 2012-2017) laurent.besac...@imag.fr -------------------------
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