Search Results for author: Ludovica Pannitto

Found 7 papers, 2 papers with code

CALaMo: a Constructionist Assessment of Language Models

no code implementations7 Feb 2023 Ludovica Pannitto, Aurélie Herbelot

This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach.

Philosophy

Teaching NLP with Bracelets and Restaurant Menus: An Interactive Workshop for Italian Students

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.

A dissemination workshop for introducing young Italian students to NLP

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.

Recurrent babbling: evaluating the acquisition of grammar from limited input data

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.

Are Word Embeddings Really a Bad Fit for the Estimation of Thematic Fit?

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.

Word Embeddings

A Structured Distributional Model of Sentence Meaning and Processing

no code implementations17 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.

Sentence Word Embeddings

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