48 citations · 65 across the 10 of their papers we have counts for
11 papers
Who is the richest club in the championship? Detecting and Rewriting Underspecified Questions Improve QA Performance
Yunchong Huang, Gianni Barlacchi, Sandro Pezzelle
Large language models (LLMs) perform well on well-posed questions, yet standard question-answering (QA) benchmarks remain far from solved. We argue that this gap is partly due to u…
Strong and Efficient Baselines for Open Domain Conversational Question Answering
Andrei C. Coman, Gianni Barlacchi, Adrià de Gispert
Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both effic…
Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey
Xiaoyu Shen, Svitlana Vakulenko, Marco del Tredici +3
Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-a…
From Rewriting to Remembering: Common Ground for Conversational QA Models
Marco Del Tredici, Xiaoyu Shen, Gianni Barlacchi +2
In conversational QA, models have to leverage information in previous turns to answer upcoming questions. Current approaches, such as Question Rewriting, struggle to extract releva…
Trajectory Test-Train Overlap in Next-Location Prediction Datasets
Massimiliano Luca, Luca Pappalardo, Bruno Lepri +1
Next-location prediction, consisting of forecasting a user's location given their historical trajectories, has important implications in several fields, such as urban planning, geo…
Leveraging Mobile Phone Data for Migration Flows
Massimiliano Luca, Gianni Barlacchi, Nuria Oliver +1
Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is…
A Survey on Deep Learning for Human Mobility
Massimiliano Luca, Gianni Barlacchi, Bruno Lepri +1
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The prolifer…
Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
Filippo Simini, Gianni Barlacchi, Massimiliano Luca +1
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment.…
Scikit-mobility: a Python library for the analysis, generation and risk assessment of mobility data
Luca Pappalardo, Filippo Simini, Gianni Barlacchi +1
The last decade has witnessed the emergence of massive mobility data sets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media plat…
Weak nodes detection in urban transport systems: Planning for resilience in Singapore
Michele Ferretti, Gianni Barlacchi, Luca Pappalardo +2
The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in resp…
Modeling Taxi Drivers' Behaviour for the Next Destination Prediction
Alberto Rossi, Gianni Barlacchi, Monica Bianchini +1
In this paper, we study how to model taxi drivers' behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey…