Unsupervised domain adaptation for neural search -NAVER LABS Europe seminar

NAVER LABS Europe (Grenoble, France) is the biggest industrial AI research lab in France with fundamental and applied research in the fields of machine learning and optimization, computer vision, natural language processing and human robot interaction. The main area of application  is  AI for Robotics where they work closely with  NAVER LABS Korea the R&D subsidiary of NAVER, Korea’s leading internet company. NAVER LABS is responsible for creating future technology in robotics, AI, autonomous driving, 3D/HD mapping and AR.LinkedIn-Bluesky

NAVER LABS Europe

Unsupervised Domain Adaptation for Neural Search. Speaker: Nils Reimers

About the speaker: The research focus of Nils Reimers is on representation learning &amp; neural search. During his post-doc at the UKP Lab at TU Darmstadt, he authored Sentence-BERT, which later resulted in the popular sentence-transformers library that allows to compute state-of-the-art text and image embeddings. In his research, he focuses on making text embeddings &amp; neural search more widely accessible with a focus on domain adaptation and multilinguality. In May 2021, Nils joined HuggingFace to lead the research team on Neural Search and to further enhance sentence-transformers.********************************************************Abstract: After textual information retrieval has stalled for many years, pre-trained transformer networks gave a big performance boost resulting in extremely better search results. However, so far these approaches require large amount of training data which is seldom available for many use-cases. In this talk, I will start with an overview of different neural search approach. I will then present BEIR, a benchmark that test neural search methods in an out-of-domain setting. As the benchmark reveals, many architecture are sensitive to domain shifts limiting their usefulness for many real word applications. To overcome this short-coming, we created Generative Pseudo Labeling (GPL), a method that transfers knowledge from slow, but robust architectures, to fast but domain-sensitive approaches, which results in highly improved search quality.

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Unsupervised domain adaptation for neural search -NAVER LABS Europe seminar

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