no code implementations • EURALI (LREC) 2022 • Irene Sucameli, Michele De Quattro, Arash Eshghi, Alessandro Suglia, Maria Simi
Since the advent of Transformer-based, pretrained language models (LM) such as BERT, Natural Language Understanding (NLU) components in the form of Dialogue Act Recognition (DAR) and Slot Recognition (SR) for dialogue systems have become both more accurate and easier to create for specific application domains.
1 code implementation • COLING 2022 • Michael Hanna, Federico Pedeni, Alessandro Suglia, Alberto Testoni, Raffaella Bernardi
This paves the way for a systematic way of evaluating embodied AI agents that understand grounded actions.
no code implementations • ACL 2022 • George Pantazopoulos, Alessandro Suglia, Arash Eshghi
Compositionality – the ability to combine simpler concepts to understand & generate arbitrarily more complex conceptual structures – has long been thought to be the cornerstone of human language capacity.
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.
1 code implementation • 7 May 2024 • Georgios Pantazopoulos, Amit Parekh, Malvina Nikandrou, Alessandro Suglia
Augmenting Large Language Models (LLMs) with image-understanding capabilities has resulted in a boom of high-performing Vision-Language models (VLMs).
1 code implementation • 21 Apr 2024 • Georgios Pantazopoulos, Alessandro Suglia, Oliver Lemon, Arash Eshghi
In this paper, we use \textit{diagnostic classifiers} to measure the extent to which the visual prompt produced by the resampler encodes spatial information.
no code implementations • 6 Jan 2024 • Yintao Tai, Xiyang Liao, Alessandro Suglia, Antonio Vergari
However, these pixel-based LLMs are limited to discriminative tasks (e. g., classification) and, similar to BERT, cannot be used to generate text.
1 code implementation • 7 Dec 2023 • Sabrina McCallum, Max Taylor-Davies, Stefano V. Albrecht, Alessandro Suglia
Despite numerous successes, the field of reinforcement learning (RL) remains far from matching the impressive generalisation power of human behaviour learning.
no code implementations • 5 Dec 2023 • Alessandro Suglia, Ioannis Konstas, Oliver Lemon
Our analysis of the literature provides evidence that future work should be focusing on interactive games where communication in Natural Language is important to resolve ambiguities about object referents and action plans and that physical embodiment is essential to understand the semantics of situations and events.
no code implementations • 7 Nov 2023 • Georgios Pantazopoulos, Malvina Nikandrou, Amit Parekh, Bhathiya Hemanthage, Arash Eshghi, Ioannis Konstas, Verena Rieser, Oliver Lemon, Alessandro Suglia
Interactive and embodied tasks pose at least two fundamental challenges to existing Vision & Language (VL) models, including 1) grounding language in trajectories of actions and observations, and 2) referential disambiguation.
1 code implementation • 28 Jul 2023 • Javier Chiyah-Garcia, Alessandro Suglia, Arash Eshghi, Helen Hastie
Referential ambiguities arise in dialogue when a referring expression does not uniquely identify the intended referent for the addressee.
1 code implementation • 8 Nov 2022 • Alessandro Suglia, José Lopes, Emanuele Bastianelli, Andrea Vanzo, Shubham Agarwal, Malvina Nikandrou, Lu Yu, Ioannis Konstas, Verena Rieser
As the course of a game is unpredictable, so are commentaries, which makes them a unique resource to investigate dynamic language grounding.
no code implementations • 30 Sep 2022 • Mavina Nikandrou, Lu Yu, Alessandro Suglia, Ioannis Konstas, Verena Rieser
We first propose three plausible task formulations and demonstrate their impact on the performance of continual learning algorithms.
2 code implementations • 25 Feb 2022 • Javier Chiyah-Garcia, Alessandro Suglia, José Lopes, Arash Eshghi, Helen Hastie
Anaphoric expressions, such as pronouns and referential descriptions, are situated with respect to the linguistic context of prior turns, as well as, the immediate visual environment.
1 code implementation • 10 Aug 2021 • Alessandro Suglia, Qiaozi Gao, Jesse Thomason, Govind Thattai, Gaurav Sukhatme
Language-guided robots performing home and office tasks must navigate in and interact with the world.
no code implementations • EACL 2021 • Alessandro Suglia, Yonatan Bisk, Ioannis Konstas, Antonio Vergari, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
Guessing games are a prototypical instance of the "learning by interacting" paradigm.
no code implementations • COLING 2020 • Alessandro Suglia, Antonio Vergari, Ioannis Konstas, Yonatan Bisk, Emanuele Bastianelli, Andrea Vanzo, Oliver Lemon
However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time.
no code implementations • ACL 2020 • Alessandro Suglia, Ioannis Konstas, Andrea Vanzo, Emanuele Bastianelli, Desmond Elliott, Stella Frank, Oliver Lemon
To remedy this, we present GROLLA, an evaluation framework for Grounded Language Learning with Attributes with three sub-tasks: 1) Goal-oriented evaluation; 2) Object attribute prediction evaluation; and 3) Zero-shot evaluation.
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.