575 citations · 1.7k across the 39 of their papers we have counts for
8 papers · 1 filter
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
HongChien Yu, Chenyan Xiong, Jamie Callan
Dense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encod…
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Luyu Gao, Jamie Callan
Recent research demonstrates the effectiveness of using fine-tuned language models~(LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavil…
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
Luyu Gao, Zhuyun Dai, Jamie Callan
Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index. Recent neural IR models shifts towards s…
Condenser: a Pre-training Architecture for Dense Retrieval
Luyu Gao, Jamie Callan
Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passa…
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
Luyu Gao, Zhuyun Dai, Jamie Callan
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextual…
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Luís Borges, Bruno Martins, Jamie Callan
Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural mod…
PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer
HongChien Yu, Zhuyun Dai, Jamie Callan
Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefi…
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup
Luyu Gao, Yunyi Zhang, Jiawei Han +1
Contrastive learning has been applied successfully to learn vector representations of text. Previous research demonstrated that learning high-quality representations benefits from…