no code implementations • CLASP 2022 • Claudio Greco, Alberto Testoni, Raffaella Bernardi, Stella Frank
Pre-trained Vision and Language Transformers achieve high performance on downstream tasks due to their ability to transfer representational knowledge accumulated during pretraining on substantial amounts of data.
no code implementations • SIGDIAL (ACL) 2022 • Alessandro Suglia, Bhathiya Hemanthage, Malvina Nikandrou, George Pantazopoulos, Amit Parekh, Arash Eshghi, Claudio Greco, Ioannis Konstas, Oliver Lemon, Verena Rieser
We demonstrate EMMA, an embodied multimodal agent which has been developed for the Alexa Prize SimBot challenge.
no code implementations • EMNLP (SpLU) 2020 • Alberto Testoni, Claudio Greco, Tobias Bianchi, Mauricio Mazuecos, Agata Marcante, Luciana Benotti, Raffaella Bernardi
By analyzing LXMERT errors and its attention mechanisms, we find that our classification helps to gain a better understanding of the skills required to answer different spatial questions.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Sandro Pezzelle, Claudio Greco, Greta Gandolfi, Eleonora Gualdoni, Raffaella Bernardi
This paper introduces BD2BB, a novel language and vision benchmark that requires multimodal models combine complementary information from the two modalities.
no code implementations • ACL 2019 • Claudio Greco, Barbara Plank, Raquel Fernández, Raffaella Bernardi
We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA).
1 code implementation • COLING 2018 • Hoa Trong Vu, Claudio Greco, Aliia Erofeeva, Somayeh Jafaritazehjan, Guido Linders, Marc Tanti, Alberto Testoni, Raffaella Bernardi, Albert Gatt
Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics.
Ranked #2 on Natural Language Inference on V-SNLI
no code implementations • 8 Feb 2017 • Claudio Greco, Alessandro Suglia, Pierpaolo Basile, Gaetano Rossiello, Giovanni Semeraro
People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences.