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Towards Deeper Language Understanding in Large-Scale Information Retrieval
Information retrieval (IR) systems face large document collections and diverse language patterns. It brings challenges for using advanced NLP techniques in retrieval. In this talk, I will share our efforts in using deep neural networks to improve language understanding in IR. First, I will share our work in applying Transformers to generate context-aware bag-of-words document representations. These context-aware representations significantly boost retrieval accuracy while the simple bag-of-words ensures efficiency. Next, I will share our recent explorations in complementing bag-of-words with machine-learned embeddings, and show how our method is used in a zero-shot setting to help people access COVID-19 articles.