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Alessandro Lenci

10 accepted papers

2025

All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark

Davide Testa, Giovanni Bonetta, Raffaella Bernardi, Alessandro Bondielli, Alessandro Lenci, Alessio Miaschi +2
EMNLP 2025

We introduce MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos. MAIA differs from other available video benchmarks for its design, its reasoning categories, the metric it uses, and the la

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2025

ExpliCa: Evaluating Explicit Causal Reasoning in Large Language Models

Martina Miliani, Serena Auriemma, Alessandro Bondielli, Emmanuele Chersoni, Lucia Passaro, Irene Sucameli +1
ACL 2025finding

Large Language Models (LLMs) are increasingly used in tasks requiring interpretive and inferential accuracy. In this paper, we introduce ExpliCa, a new dataset for evaluating LLMs in explicit causal reasoning. ExpliCa uniquely integrates both causal and temporal relations presented in different ling…

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2024

Comparing Static and Contextual Distributional Semantic Models on Intrinsic Tasks: An Evaluation on Mandarin Chinese Datasets

A Pranav, Yan Cong, Emmanuele Chersoni, Yu-Yin Hsu, Alessandro Lenci
COLING 2024main

The field of Distributional Semantics has recently undergone important changes, with the contextual representations produced by Transformers taking the place of static word embeddings models. Noticeably, previous studies comparing the two types of vectors have only focused on the English language an…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2023

We Understand Elliptical Sentences, and Language Models should Too: A New Dataset for Studying Ellipsis and its Interaction with Thematic Fit

Davide Testa, Emmanuele Chersoni, Alessandro Lenci
ACL 2023long

Ellipsis is a linguistic phenomenon characterized by the omission of one or more sentence elements. Solving such a linguistic construction is not a trivial issue in natural language processing since it involves the retrieval of non-overtly expressed verbal material, which might in turn require the m…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2022

Does BERT Recognize an Agent? Modeling Dowty’s Proto-Roles with Contextual Embeddings

Mattia Proietti, Gianluca Lebani, Alessandro Lenci
COLING 2022main

Contextual embeddings build multidimensional representations of word tokens based on their context of occurrence. Such models have been shown to achieve a state-of-the-art performance on a wide variety of tasks. Yet, the community struggles in understanding what kind of semantic knowledge these repr…

Cited by 6Source ↗PDF ↗Scholar ↗
2022

Does BERT really agree ? Fine-grained Analysis of Lexical Dependence on a Syntactic Task

Karim Lasri, Alessandro Lenci, Thierry Poibeau
ACL 2022findings

Although transformer-based Neural Language Models demonstrate impressive performance on a variety of tasks, their generalization abilities are not well understood. They have been shown to perform strongly on subject-verb number agreement in a wide array of settings, suggesting that they learned to t…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗
2022

Probing for the Usage of Grammatical Number

Karim Lasri, Tiago Pimentel, Alessandro Lenci, Thierry Poibeau, Ryan Cotterell
ACL 2022long

A central quest of probing is to uncover how pre-trained models encode a linguistic property within their representations. An encoding, however, might be spurious—i.e., the model might not rely on it when making predictions. In this paper, we try to find an encoding that the model actually uses, int…

Cited by 59Source ↗PDF ↗Scholar ↗
2022

Subject Verb Agreement Error Patterns in Meaningless Sentences: Humans vs. BERT

Karim Lasri, Olga Seminck, Alessandro Lenci, Thierry Poibeau
COLING 2022main

Both humans and neural language models are able to perform subject verb number agreement (SVA). In principle, semantics shouldn’t interfere with this task, which only requires syntactic knowledge. In this work we test whether meaning interferes with this type of agreement in English in syntactic str…

Cited by 4Source ↗PDF ↗Scholar ↗
2020

Don’t Invite BERT to Drink a Bottle: Modeling the Interpretation of Metonymies Using BERT and Distributional Representations

Paolo Pedinotti, Alessandro Lenci
COLING 2020main

In this work, we carry out two experiments in order to assess the ability of BERT to capture the meaning shift associated with metonymic expressions. We test the model on a new dataset that is representative of the most common types of metonymy. We compare BERT with the Structured Distributional Mod…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗