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Retrieval augmented zero-shot text classification
Zero-shot text learning enables text classifiers to handle unseen classes efficiently,
alleviating the need for task-specific training data. A simple approach often relies on …
alleviating the need for task-specific training data. A simple approach often relies on …
Augmenting passage representations with query generation for enhanced cross-lingual dense retrieval
Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language
models (PLMs) need to be trained to encompass both the relevance matching task and the …
models (PLMs) need to be trained to encompass both the relevance matching task and the …
KEIR@ ECIR 2024: The first workshop on knowledge-enhanced information retrieval
The infusion of external knowledge bases into IR models can provide enhanced ranking
results and greater interpretability, offering substantial advancements in the field. The first …
results and greater interpretability, offering substantial advancements in the field. The first …
ReFIT: Relevance Feedback from a Reranker during Inference
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-
encoder network initially retrieves a pre-defined number of candidates (eg, K= 100), which …
encoder network initially retrieves a pre-defined number of candidates (eg, K= 100), which …
Online Distillation for Pseudo-Relevance Feedback
Model distillation has emerged as a prominent technique to improve neural search models.
To date, distillation taken an offline approach, wherein a new neural model is trained to …
To date, distillation taken an offline approach, wherein a new neural model is trained to …
ReFIT: Reranker Relevance Feedback during Inference
Retrieve-and-rerank is a prevalent framework in neural information retrieval, wherein a bi-
encoder network initially retrieves a pre-defined number of candidates (eg, K= 100), which …
encoder network initially retrieves a pre-defined number of candidates (eg, K= 100), which …