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20202025
most citedConversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models

36 citations · 97 across the 15 of their papers we have counts for

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Showing cs.IRShow all

10 papers · 1 filter

cs.IR2024★ 1 cited

Synergistic Approach for Simultaneous Optimization of Monolingual, Cross-lingual, and Multilingual Information Retrieval

Adel Elmahdy, Sheng-Chieh Lin, Amin Ahmad

Information retrieval across different languages is an increasingly important challenge in natural language processing. Recent approaches based on multilingual pre-trained language…

cs.IR2024★ 1 cited

Unifying Multimodal Retrieval via Document Screenshot Embedding

Xueguang Ma, Sheng-Chieh Lin, Minghan Li +2

In the real world, documents are organized in different formats and varied modalities. Traditional retrieval pipelines require tailored document parsing techniques and content extr…

cs.IR2023★ 3 cited

Improving Conversational Passage Re-ranking with View Ensemble

Jia-Huei Ju, Sheng-Chieh Lin, Ming-Feng Tsai +1

This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality o…

cs.IR2023★ 4 cited

How to Train Your DRAGON: Diverse Augmentation Towards Generalizable Dense Retrieval

Sheng-Chieh Lin, Akari Asai, Minghan Li +5

Various techniques have been developed in recent years to improve dense retrieval (DR), such as unsupervised contrastive learning and pseudo-query generation. Existing DRs, however…

cs.IR2023★ 11 cited

SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes

Minghan Li, Sheng-Chieh Lin, Xueguang Ma +1

This paper introduces Sparsified Late Interaction for Multi-vector (SLIM) retrieval with inverted indexes. Multi-vector retrieval methods have demonstrated their effectiveness on v…

cs.IR2022★ 3 cited

CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval

Minghan Li, Sheng-Chieh Lin, Barlas Oguz +5

Multi-vector retrieval methods combine the merits of sparse (e.g. BM25) and dense (e.g. DPR) retrievers and have achieved state-of-the-art performance on various retrieval tasks. T…