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Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track

2020-11-14 19:03:24
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell
arXiv_AI
     
Abstract

We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explicit term matching to complement matching based on learned representations (i.e., the "Duet principle"), (ii) query term independence (i.e., the "QTI assumption") to scale the model to the full retrieval setting, and (iii) the ORCAS click data as an additional document description field. We find evidence which supports that all three aforementioned strategies can lead to improved retrieval quality.

Abstract (translated)

URL

https://arxiv.org/abs/2011.07368

PDF

https://arxiv.org/pdf/2011.07368.pdf

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