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Found 9 papers, 2 papers with code
SPLATE: Sparse Late Interaction Retrieval
The late interaction paradigm introduced with ColBERT stands out in the neural Information Retrieval space, offering a compelling effectiveness-efficiency trade-off across many benchmarks.
A Thorough Comparison of Cross-Encoders and LLMs for Reranking SPLADE
We present a comparative study between cross-encoder and LLMs rerankers in the context of re-ranking effective SPLADE retrievers.
SPLADE-v3: New baselines for SPLADE
A companion to the release of the latest version of the SPLADE library.
Benchmarking Middle-Trained Language Models for Neural Search
We compare both dense and sparse approaches under various finetuning protocols and middle training on different collections (MS MARCO, Wikipedia or Tripclick).
Parameter-Efficient Sparse Retrievers and Rerankers using Adapters
While previous studies have only experimented with dense retriever or in a cross lingual retrieval scenario, in this paper we aim to complete the picture on the use of adapters in IR.
An Experimental Study on Pretraining Transformers from Scratch for IR
In this paper, we study the impact of the pretraining collection on the final IR effectiveness.
LayoutXLM vs. GNN: An Empirical Evaluation of Relation Extraction for Documents
This paper investigates the Relation Extraction task in documents by benchmarking two different neural network models: a multi-modal language model (LayoutXLM) and a Graph Neural Network: Edge Convolution Network (ECN).
Comparing Machine Learning Approaches for Table Recognition in Historical Register Books
We present in this paper experiments on Table Recognition in hand-written registry books.
Bench-Marking Information Extraction in Semi-Structured Historical Handwritten Records
In this report, we present our findings from benchmarking experiments for information extraction on historical handwritten marriage records Esposalles from IEHHR - ICDAR 2017 robust reading competition.
