Ad-hoc information retrieval refers to the task of returning information resources related to a user query formulated in natural language.
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The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.
We present PARADE, an end-to-end Transformer-based model that considers document-level context for document reranking.
Ranked #2 on Ad-Hoc Information Retrieval on TREC Robust04
While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages.
Since most standard ad-hoc information retrieval datasets publicly available for academic research (e. g. Robust04, ClueWeb09) have at most 250 annotated queries, the recent deep learning models for information retrieval perform poorly on these datasets.
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.
Ranked #5 on Ad-Hoc Information Retrieval on TREC Robust04
Following recent successes in applying BERT to question answering, we explore simple applications to ad hoc document retrieval.
Ranked #2 on Ad-Hoc Information Retrieval on TREC Robust04 (MAP metric)
A cascaded ranking architecture turns ranking into a pipeline of multiple stages, and has been shown to be a powerful approach to balancing efficiency and effectiveness trade-offs in large-scale search systems.