IR from Bag-of-words to BERT and Beyond with PyTerrier


Advances from the natural language processing community have recently sparked a renaissance in the task of adhoc search. Particularly, large contextualized language modeling techniques, such as BERT, have equipped ranking models with a far deeper understanding of language than the capabilities of previous bag-of-words (BoW) models. Applying these techniques to a new task is tricky, requiring knowledge of deep learning frameworks, and significant scripting and data munging. In this full-day tutorial, we build up from foundational retrieval principles to the latest neural ranking techniques. We provide background on classical (e.g., BoW), modern (e.g., Learning to Rank) and contemporary (e.g., BERT search ranking and re-ranking techniques. Going further, we detail and demonstrate how these can be easily experimentally applied to new search tasks in a new declarative style of conducting experiments exemplified by the PyTerrier and OpenNIR search toolkits.

1 Jan 0001
Conference Tutorial
30th ACM Conference of Information and Knowledge Management (CIKM 2021)