NAVER LABS Europe is the biggest industrial AI research lab in France. As the research arm of Korea's leading internet services company, NAVER, the mission of LABS is to create future technologies and new ways to interact and connect to the digital and real world. This includes research in neural systems to give humans faster and better access to information and knowledge.
To achieve this, we need to dramatically improve information access and question answering. Over the last few years, the paradigm shift of deep learning and self-attention networks in data science has dramatically improved performance over the full spectrum of applications and, optimized through self-supervised encoding tasks, these networks have allowed for the building of both syntactic and semantic a priori knowledge for natural language understanding and generation tasks. Besides these representation capabilities, it has been shown that pre-trained, large-scale parametric language models capture, as a side effect, part of the information enclosed in texts used for their training. As such, they tend to keep question-answering capabilities using cloze-form statements or open-domain formulations for masked or causal language model approaches respectively.
In this context, NAVER LABS Europe is looking for a motivated, post-doctoral researcher to develop novel modelisation and learning approaches to address the problem of handling compositional questions with possible applications to NAVER Question-Answering services.
Parametric information storage and retrieval has been studied in other contexts such as in images, and constitutes a promising research direction for fundamental machine learning. During this post-doctoral study, the successful candidate will first conduct an exhaustive evaluation of the capabilities of parametric language models as a knowledge base before developing novel modelisation, learning and adaption paradigms to enhance the usability of such models in challenging question-answering settings.
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