Standard bag-of-words term-matching techniques in document retrieval fail to exploit rich
semantic information embedded in the document texts. One promising recent trend in …

This paper applies BERT to ad hoc document retrieval on news articles, which requires
addressing two challenges: relevance judgments in existing test collections are typically …

Pretrained language models (PLMs) exemplified by BERT have proven to be remarkably
effective for ad hoc ranking. As opposed to pre-BERT models that required specialized …

Document Retrieval has seen significant advancements in the last few decades. Latest
developments in Natural Language Processing have made it possible to incorporate context …

We present Birch, a system that applies BERT to document retrieval via integration with the
open-source Anserini information retrieval toolkit to demonstrate end-to-end search over …

Online search latency is a major bottleneck in deploying large-scale pre-trained language
models, eg BERT, in retrieval applications. Inspired by the recent advances in transformer …

Recent work has shown the surprising ability of multi-lingual BERT to serve as a zero-shot
cross-lingual transfer model for a number of language processing tasks. We combine this …

Pre-trained contextual language models such as BERT, GPT, and XLnet work quite well for
document retrieval tasks. Such models are fine-tuned based on the query-document/query …

This paper describes Brown University's submission to the TREC 2019 Deep Learning track.
We followed a 2-phase method for producing a ranking of passages for a given input query …

Nowadays, pre-trained language models such as Bidirectional Encoder Representations
from Transformer (BERT) are becoming a basic building block in Information Retrieval tasks …