This paper describes the TOKOFOU system, an ensemble model for misinformation detection tasks based on six different transformer-based pre-trained encoders, implemented in the context of the COVID-19 Infodemic Shared Task for English. We fine tune each model on each of the task's questions and aggregate their prediction scores using a majority voting approach. TOKOFOU obtains an overall F1 score of 89.7%, ranking first.

Service robots should be able to interact naturally with non-expert human users, not only to help them in various tasks, but also to receive guidance in order to resolve ambiguities that might be present in the instruction. We consider the task of visual grounding, where the agent segments an object from a crowded scene given a natural language description. Modern holistic approaches to visual grounding usually ignore language structure and struggle to cover generic domains, therefore relying heavily on large datasets. Additionally, their transfer performance in RGB-D datasets suffers due to high visual discrepancy between the benchmark and the target domains. Modular approaches marry learning with domain modeling and exploit the compositional nature of language to decouple visual representation from language parsing, but either rely on external parsers or are trained in an end-to-end fashion due to the lack of strong supervision. In this work, we seek to tackle these limitations by introducing a fully decoupled modular framework for compositional visual grounding of entities, attributes and spatial relations. We exploit rich scene graph annotations generated in a synthetic domain and train each module independently in simulation. Our approach is evaluated both in simulation and in two real RGB-D scene datasets. Experimental results show that the decoupled nature of our framework allows for easy integration with domain adaptation approaches for Sim-To-Real visual recognition, offering a data-efficient, robust, and interpretable solution to visual grounding in robotic applications. * We thank NVIDIA Corporation for their generous donation of GPUs which was partially used in this research.

The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and compare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are implemented across these countries. We evaluated multiple multi-label classifiers on a manually annotated corpus at sentence level. The XLM-RoBERTa model achieves highest performance on this multilingual multi-label classification task, with a macro-average F1 score of 59.8%.