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Found 4 papers, 2 papers with code
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
Query-aware Tip Generation for Vertical Search
Existing work on tip generation does not take query into consideration, which limits the impact of tips in search scenarios.
PARADE: Passage Representation Aggregation for Document Reranking
In this work, we explore strategies for aggregating relevance signals from a document's passages into a final ranking score.
Ranked #2 on
Ad-Hoc Information Retrieval
on TREC Robust04
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of traditional information retrieval (IR) models by using top-ranked documents to identify and weight new query terms, thereby reducing the effect of query-document vocabulary mismatches.
Ranked #9 on
Ad-Hoc Information Retrieval
on TREC Robust04
