Semantic models for the first-stage retrieval: A comprehensive review
Multi-stage ranking pipelines have been a practical solution in modern search systems,
where the first-stage retrieval is to return a subset of candidate documents and latter stages …
where the first-stage retrieval is to return a subset of candidate documents and latter stages …
Colbert: Efficient and effective passage search via contextualized late interaction over bert
Recent progress in Natural Language Understanding (NLU) is driving fast-paced advances
in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for …
in Information Retrieval (IR), largely owed to fine-tuning deep language models (LMs) for …
Overview of the TREC 2019 deep learning track
The Deep Learning Track is a new track for TREC 2019, with the goal of studying ad hoc
ranking in a large data regime. It is the first track with large human-labeled training sets …
ranking in a large data regime. It is the first track with large human-labeled training sets …
Passage Re-ranking with BERT
R Nogueira, K Cho - arXiv preprint arXiv:1901.04085, 2019 - arxiv.org
Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et
al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved …
al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved …
Sparse, dense, and attentional representations for text retrieval
Dual encoders perform retrieval by encoding documents and queries into dense low-
dimensional vectors, scoring each document by its inner product with the query. We …
dimensional vectors, scoring each document by its inner product with the query. We …
Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges
Recent years have witnessed a substantial increase in the use of deep learning to solve
various natural language processing (NLP) problems. Early deep learning models were …
various natural language processing (NLP) problems. Early deep learning models were …
Multi-stage document ranking with BERT
The advent of deep neural networks pre-trained via language modeling tasks has spurred a
number of successful applications in natural language processing. This work explores one …
number of successful applications in natural language processing. This work explores one …
A deep look into neural ranking models for information retrieval
Ranking models lie at the heart of research on information retrieval (IR). During the past
decades, different techniques have been proposed for constructing ranking models, from …
decades, different techniques have been proposed for constructing ranking models, from …
Few-shot conversational dense retrieval
Dense retrieval (DR) has the potential to resolve the query understanding challenge in
conversational search by matching in the learned embedding space. However, this …
conversational search by matching in the learned embedding space. However, this …
TREC CAsT 2019: The conversational assistance track overview
The Conversational Assistance Track (CAsT) is a new track for TREC 2019 to facilitate
Conversational Information Seeking (CIS) research and to create a large-scale reusable test …
Conversational Information Seeking (CIS) research and to create a large-scale reusable test …