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Found 3 papers, 1 papers with code
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs).
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track.
Conformer-Kernel with Query Term Independence for Document Retrieval
In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption.
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