no code implementations • 7 Feb 2023 • Ludovica Pannitto, Aurélie Herbelot
This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach.
1 code implementation • NAACL (TeachingNLP) 2021 • Ludovica Pannitto, Lucia Busso, Claudia Roberta Combei, Lucio Messina, Alessio Miaschi, Gabriele Sarti, Malvina Nissim
To raise awareness, curiosity, and longer-term interest in young people, we have developed an interactive workshop designed to illustrate the basic principles of NLP and computational linguistics to high school Italian students aged between 13 and 18 years.
1 code implementation • NAACL (TeachingNLP) 2021 • Lucio Messina, Lucia Busso, Claudia Roberta Combei, Ludovica Pannitto, Alessio Miaschi, Gabriele Sarti, Malvina Nissim
We describe and make available the game-based material developed for a laboratory run at several Italian science festivals to popularize NLP among young students.
no code implementations • CONLL 2020 • Ludovica Pannitto, Aurélie Herbelot
Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input.
no code implementations • LREC 2020 • Emmanuele Chersoni, Ludovica Pannitto, Enrico Santus, Aless Lenci, ro, Chu-Ren Huang
While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models.
no code implementations • 17 Jun 2019 • Emmanuele Chersoni, Enrico Santus, Ludovica Pannitto, Alessandro Lenci, Philippe Blache, Chu-Ren Huang
In this paper, we propose a Structured Distributional Model (SDM) that combines word embeddings with formal semantics and is based on the assumption that sentences represent events and situations.
no code implementations • SEMEVAL 2017 • Giuseppe Attardi, Antonio Carta, Federico Errica, Andrea Madotto, Ludovica Pannitto
In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017.