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Found 9 papers, 4 papers with code
Data Augmentation for Sample Efficient and Robust Document Ranking
We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model.
Efficient Neural Ranking using Forward Indexes and Lightweight Encoders
Dual-encoder-based dense retrieval models have become the standard in IR.
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval
Dual-encoder-based neural retrieval models achieve appreciable performance and complement traditional lexical retrievers well due to their semantic matching capabilities, which makes them a common choice for hybrid IR systems.
Supervised Contrastive Learning Approach for Contextual Ranking
This paper proposes a simple yet effective method to improve ranking performance on smaller datasets using supervised contrastive learning for the document ranking problem.
Efficient Neural Ranking using Forward Indexes
In this paper, we propose the Fast-Forward index -- a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores -- as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search.
Extractive Explanations for Interpretable Text Ranking
We introduce the Select-and-Rank paradigm for document ranking, where we first output an explanation as a selected subset of sentences in a document.
Exploiting Sentence-Level Representations for Passage Ranking
Recently, pre-trained contextual models, such as BERT, have shown to perform well in language related tasks.
Boilerplate Removal using a Neural Sequence Labeling Model
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing.
Node Representation Learning for Directed Graphs
We also unearth the limitations of evaluations on directed graphs in previous works and propose a clear strategy for evaluating link prediction and graph reconstruction in directed graphs.
