24 citations · 28 across the 5 of their papers we have counts for
5 papers
QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Ranking
Shubham Chatterjee, Jeff Dalton
Neural IR has advanced through two distinct paths: entity-oriented approaches leveraging knowledge graphs and multi-vector models capturing fine-grained semantics. We introduce QDE…
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities
Thong Nguyen, Shubham Chatterjee, Sean MacAvaney +3
Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. Splitting entities can reduce retrieval…
Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search
Hideaki Joko, Shubham Chatterjee, Andrew Ramsay +3
The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dia…
DREQ: Document Re-Ranking Using Entity-based Query Understanding
Shubham Chatterjee, Iain Mackie, Jeff Dalton
While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on…
GRILLBot: An Assistant for Real-World Tasks with Neural Semantic Parsing and Graph-Based Representations
Carlos Gemmell, Iain Mackie, Paul Owoicho +3
GRILLBot is the winning system in the 2022 Alexa Prize TaskBot Challenge, moving towards the next generation of multimodal task assistants. It is a voice assistant to guide users t…