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PhD Fellowships to start in 2014
Expired June 16!
The HLT unit of FBK will sponsor three project specific grants for doctoral students starting with the A.Y. 2014-2015. Doctorate fellowships will be formally pursued at the ICT International Doctorate School of the University of Trento. The program starts between September and November 2014, has a minimum length of three years and includes attendance of courses during the first two years, while the third is fully dedicated to research work.
The opening of the call is expected at the beginning of May 2014. Potential candidates are invited to contact us in advance for preliminary interviews. There is also the possibility to begin with an internships at our group during the Summer before the official PhD program starts. The call closes on 16 June 2014.
Topic: Machine Translation
Title: Human in the loop for advanced machine translation
Nowadays, human translation and machine translation are no longer antithetical opposites. Rather, the two worlds are getting closer and started to complement each other. On one side, the evolution of translation industry is witnessing a clear trend towards the adoption of Machine Translation (MT) as a primary support to professional translators. On the other side, the variety of data that can be collected from human feedback provides to MT research an unprecedented wealth of knowledge about the dynamics (practical and cognitive) of the translation process. The future is a symbiotic scenario where humans are assisted by reliable MT technology that, at the same time, continuously evolves by learning from translators activity. This grant aims to transform this vision into reality. The candidate will team up a world-class research effort developing new MT technology capable to integrate information obtained unobtrusively from real professional translation workflows. Relevant topics include: i) the extraction and generalization of knowledge (e.g. translation and correction strategies) from different types of human feedback, ii) projecting the acquired knowledge onto the core MT components, iii) modeling cognitive aspects of the translation process, iv) evaluating the effect of machine translation on human translation.
Topic: Automatic Speech Recognition
Title: Acoustic Modeling for Speech Recognition
FBK has been pursuing research in automatic speech recognition (ASR) for two decades with the goal to develop state-of-the-art technology for interactive- and found-speech recognition, and to address applications ranging from speech analytics over the phone line to transcription of speech as found in any audio/visual document. Languages on which we are working with include Italian, English, Spanish, German, Dutch, Arabic, Turkish, Russian, Portuguese and French.
Although FBK is interested in applicants in all areas of automatic transcription technology, most relevant topics for this Call are in the areas of acoustic modeling for large vocabulary ASR (which includes, for example, neural networks in ASR, building ASR systems for under-resourced languages, speaker adaptive training, methods for fast and efficient adaptation to changing application domains, data selection methods for acoustic model training), speaker diarization and spoken language detection. The candidate will team up a world-class research effort developing new ASR technology and advancing beyond the state-of-the-art, taking advantage from the large experience gained by FBK during the last 20 years.
Contacts: Diego Giuliani
Topic: Content Processing
Title: Building the Web of Data exploiting Natural Language Processing
Web of Data is about making information available on the Web accessible to machines and hence transforming how information can be found and manipulated. Of all recent initiatives oriented to create the Web of data, Wikidata is the most relevant. According to the promoters “the project aims to build a free knowledge base about the world that can be read and edited by humans and machines alike.” In this PhD the candidate is asked to investigate natural language processing and machine learning techniques that can be used to automatically contribute to Wikidata. Specifically, it will be investigated semi-supervised approaches that can bootstrap from the data already available in Wikidata and other resources such as DBpedia and Freebase. Furthermore, careful consideration will have to be given to develop approaches applicable to different languages. Finally, as Wikidata will be edited by both humans and machines, active learning could play a crucial role and open new research challenges due to the crowd-sourcing approach: will the automatic approach be able to interact with the other users during the discussion necessary to collect/approve/filter the data to publish?
Contacts: Claudio Giuliano