204 citations · 879 across the 52 of their papers we have counts for
6 papers · 1 filter
DiPair: Fast and Accurate Distillation for Trillion-Scale Text Matching and Pair Modeling
Jiecao Chen, Liu Yang, Karthik Raman +6
Pre-trained models like BERT (Devlin et al., 2018) have dominated NLP / IR applications such as single sentence classification, text pair classification, and question answering. Ho…
Leveraging Semantic and Lexical Matching to Improve the Recall of Document Retrieval Systems: A Hybrid Approach
Saar Kuzi, Mingyang Zhang, Cheng Li +2
Search engines often follow a two-phase paradigm where in the first stage (the retrieval stage) an initial set of documents is retrieved and in the second stage (the re-ranking sta…
RRF102: Meeting the TREC-COVID Challenge with a 100+ Runs Ensemble
Michael Bendersky, Honglei Zhuang, Ji Ma +3
In this paper, we report the results of our participation in the TREC-COVID challenge. To meet the challenge of building a search engine for rapidly evolving biomedical collection,…
Interpretable Learning-to-Rank with Generalized Additive Models
Honglei Zhuang, Xuanhui Wang, Michael Bendersky +7
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area. Recent progress on interpretable ranking models largely focuses on generating…
Beyond 512 Tokens: Siamese Multi-depth Transformer-based Hierarchical Encoder for Long-Form Document Matching
Liu Yang, Mingyang Zhang, Cheng Li +2
Many natural language processing and information retrieval problems can be formalized as the task of semantic matching. Existing work in this area has been largely focused on match…
Learning-to-Rank with BERT in TF-Ranking
Shuguang Han, Xuanhui Wang, Mike Bendersky +1
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank…