Search Results for author: Gianni Barlacchi

Found 14 papers, 4 papers with code

semiPQA: A Study on Product Question Answering over Semi-structured Data

no code implementations ECNLP (ACL) 2022 Xiaoyu Shen, Gianni Barlacchi, Marco del Tredici, Weiwei Cheng, Adrià Gispert

To fill in this blank, here we study how to effectively incorporate semi-structured answer sources for PQA and focus on presenting answers in a natural, fluent sentence.

Attribute Question Answering +1

Strong and Efficient Baselines for Open Domain Conversational Question Answering

no code implementations23 Oct 2023 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 efficiency and effectiveness.

Conversational Question Answering Decoder +3

Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey

no code implementations5 Aug 2022 Xiaoyu Shen, Svitlana Vakulenko, Marco del Tredici, Gianni Barlacchi, Bill Byrne, Adrià De Gispert

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-answering systems.

Open-Domain Question Answering Retrieval

Trajectory Test-Train Overlap in Next-Location Prediction Datasets

1 code implementation7 Mar 2022 Massimiliano Luca, Luca Pappalardo, Bruno Lepri, Gianni Barlacchi

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-marketing, and disease spreading.


A Survey on Deep Learning for Human Mobility

1 code implementation4 Dec 2020 Massimiliano Luca, Gianni Barlacchi, Bruno Lepri, Luca Pappalardo

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.

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information

1 code implementation1 Dec 2020 Filippo Simini, Gianni Barlacchi, Massimiliano Luca, Luca Pappalardo

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

8 code implementations8 Jul 2019 Luca Pappalardo, Gianni Barlacchi, Filippo Simini, Roberto Pellungrini

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 platforms.

Physics and Society

Modeling Taxi Drivers' Behaviour for the Next Destination Prediction

no code implementations21 Jul 2018 Alberto Rossi, Gianni Barlacchi, Monica Bianchini, Bruno Lepri

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.

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