Overview of the TREC 2020 deep learning track
arXiv:2102.07662
Abstract
This is the second year of the TREC Deep Learning Track, with the goal of studying ad hoc ranking in the large training data regime. We again have a document retrieval task and a passage retrieval task, each with hundreds of thousands of human-labeled training queries. We evaluate using single-shot TREC-style evaluation, to give us a picture of which ranking methods work best when large data is available, with much more comprehensive relevance labeling on the small number of test queries. This year we have further evidence that rankers with BERT-style pretraining outperform other rankers in the large data regime.
arXiv admin note: substantial text overlap with arXiv:2003.07820
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- To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
- Optimizing Guided Traversal for Fast Learned Sparse Retrieval
- SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes
- On the Interpolation of Contextualized Term-based Ranking with BM25 for Query-by-Example Retrieval
- Soft Prompt Decoding for Multilingual Dense Retrieval
- Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
- LexBoost: Improving Lexical Document Retrieval with Nearest Neighbors
- Human Preferences as Dueling Bandits
- Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
- Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track