16 citations · 18 across the 5 of their papers we have counts for
5 papers
Layer-wise Token Compression for Efficient Document Reranking
Shengyao Zhuang, Zhichao Xu, Ivano Lauriola
Transformer-based document cross-encoder rerankers are a central component of modern information retrieval systems. Despite their success, these models suffer from high computation…
Improving Document Retrieval Coherence for Semantically Equivalent Queries
Stefano Campese, Alessandro Moschitti, Ivano Lauriola
Dense Retrieval (DR) models have proven to be effective for Document Retrieval and Information Grounding tasks. Usually, these models are trained and optimized for improving the re…
QUADRo: Dataset and Models for QUestion-Answer Database Retrieval
Stefano Campese, Ivano Lauriola, Alessandro Moschitti
An effective paradigm for building Automated Question Answering systems is the re-use of previously answered questions, e.g., for FAQs or forum applications. Given a database (DB)…
MKLpy: a python-based framework for Multiple Kernel Learning
Ivano Lauriola, Fabio Aiolli
Multiple Kernel Learning is a recent and powerful paradigm to learn the kernel function from data. In this paper, we introduce MKLpy, a python-based framework for Multiple Kernel L…
Context-based Transformer Models for Answer Sentence Selection
Ivano Lauriola, Alessandro Moschitti
An important task for the design of Question Answering systems is the selection of the sentence containing (or constituting) the answer from documents relevant to the asked questio…