NAVER LABS Europe seminars are open to the public. This seminar is virtual and requires registration
Date: 3rd March 2022, 11:00 am (GMT +1)
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.
About the Speaker: The research focus of Nils Reimers is on representation learning & 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 & 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.
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