413 citations · 3.1k across the 143 of their papers we have counts for
16 papers · 1 filter
Sparsifying Sparse Representations for Passage Retrieval by Top- Masking
Jheng-Hong Yang, Xueguang Ma, Jimmy Lin
Sparse lexical representation learning has demonstrated much progress in improving passage retrieval effectiveness in recent models such as DeepImpact, uniCOIL, and SPLADE. This pa…
Densifying Sparse Representations for Passage Retrieval by Representational Slicing
Sheng-Chieh Lin, Jimmy Lin
Learned sparse and dense representations capture different successful approaches to text retrieval and the fusion of their results has proven to be more effective and robust. Prior…
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study
Hang Li, Shengyao Zhuang, Ahmed Mourad +3
Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve se…
Wacky Weights in Learned Sparse Representations and the Revenge of Score-at-a-Time Query Evaluation
Joel Mackenzie, Andrew Trotman, Jimmy Lin
Recent advances in retrieval models based on learned sparse representations generated by transformers have led us to, once again, consider score-at-a-time query evaluation techniqu…
Encoder Adaptation of Dense Passage Retrieval for Open-Domain Question Answering
Minghan Li, Jimmy Lin
One key feature of dense passage retrievers (DPR) is the use of separate question and passage encoder in a bi-encoder design. Previous work on generalization of DPR mainly focus on…
A Proposed Conceptual Framework for a Representational Approach to Information Retrieval
Jimmy Lin
This paper outlines a conceptual framework for understanding recent developments in information retrieval and natural language processing that attempts to integrate dense and spars…
Cross-Lingual Training with Dense Retrieval for Document Retrieval
Peng Shi, Rui Zhang, He Bai +1
Dense retrieval has shown great success in passage ranking in English. However, its effectiveness in document retrieval for non-English languages remains unexplored due to the limi…
Mr. TyDi: A Multi-lingual Benchmark for Dense Retrieval
Xinyu Zhang, Xueguang Ma, Peng Shi +1
We present Mr. TyDi, a multi-lingual benchmark dataset for mono-lingual retrieval in eleven typologically diverse languages, designed to evaluate ranking with learned dense represe…
A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques
Jimmy Lin, Xueguang Ma
Recent developments in representational learning for information retrieval can be organized in a conceptual framework that establishes two pairs of contrasts: sparse vs. dense repr…
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang +2
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community…
MS MARCO: Benchmarking Ranking Models in the Large-Data Regime
Nick Craswell, Bhaskar Mitra, Emine Yilmaz +2
Evaluation efforts such as TREC, CLEF, NTCIR and FIRE, alongside public leaderboard such as MS MARCO, are intended to encourage research and track our progress, addressing big ques…
A Replication Study of Dense Passage Retriever
Xueguang Ma, Kai Sun, Ronak Pradeep +1
Text retrieval using learned dense representations has recently emerged as a promising alternative to "traditional" text retrieval using sparse bag-of-words representations. One re…
Contextualized Query Embeddings for Conversational Search
Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin
This paper describes a compact and effective model for low-latency passage retrieval in conversational search based on learned dense representations. Prior to our work, the state-o…
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard
Jimmy Lin, Daniel Campos, Nick Craswell +2
Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as…
Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations
Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin +3
Pyserini is an easy-to-use Python toolkit that supports replicable IR research by providing effective first-stage retrieval in a multi-stage ranking architecture. Our toolkit is se…
The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models
Ronak Pradeep, Rodrigo Nogueira, Jimmy Lin
We propose a design pattern for tackling text ranking problems, dubbed "Expando-Mono-Duo", that has been empirically validated for a number of ad hoc retrieval tasks in different d…