no code implementations • SEMEVAL 2019 • Eug{\'e}nio Ribeiro, V{\^a}nia Mendon{\c{c}}a, Ricardo Ribeiro, David Martins de Matos, Alberto Sardinha, Ana L{\'u}cia Santos, Lu{\'\i}sa Coheur
We approach all the subtasks by applying a graph clustering algorithm on contextualized embedding representations of the verbs and arguments.
no code implementations • 24 Jan 2021 • Guilherme S. Varela, Pedro P. Santos, Alberto Sardinha, Francisco S. Melo
Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology.
1 code implementation • ACL 2021 • Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha, Ana Lúcia Santos
In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging.
1 code implementation • 23 Nov 2021 • Pedro P. Santos, Diogo S. Carvalho, Alberto Sardinha, Francisco S. Melo
We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms.
no code implementations • 10 Jan 2022 • João G. Ribeiro, Cassandro Martinho, Alberto Sardinha, Francisco S. Melo
In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol.
1 code implementation • 9 Mar 2022 • Vânia Mendonça, Ricardo Rei, Luisa Coheur, Alberto Sardinha
Moreover, since we not know in advance which query strategy will be the most adequate for a certain language pair and set of Machine Translation models, we propose to dynamically combine multiple strategies using prediction with expert advice.
no code implementations • 6 Apr 2022 • Fábio Vital, Miguel Vasco, Alberto Sardinha, Francisco Melo
We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e. g., visual or sound), corresponding to a sequence of instructions, to an adequate sequence of movements to be executed by a robot.
1 code implementation • 12 Oct 2022 • Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 10 Jan 2023 • Gonçalo Querido, Alberto Sardinha, Francisco S. Melo
We investigate whether a model with these characteristics is capable of achieving similar performance to state-of-the-art model-free RL agents that access the full input observation.