5 accepted papers
Understanding natural language commands in situated Human-Robot Interaction (HRI) requires linking linguistic input to perceptual context. Traditional symbolic parsers lack the flexibility to operate in complex, dynamic environments. We introduce a novel Multimodal Grounded Semantic Role Labelling (
Grounded natural language understanding in Human-Robot Interaction (HRI) requires integrating linguistic, visual, and world knowledge to ensure effective task execution. We propose an approach that enhances Multi-Modal Large Language Models (MLLMs) with a novel explicit dialogue planning phase, allo…
This paper explores Interactive Grounded Language Understanding (IGLU) challenges within Human-Robot Interaction (HRI). In this setting, a robot interprets user commands related to its environment, aiming to discern whether a specific command can be executed. If faced with ambiguities or incomplete…
Even if recent Transformer-based architectures, such as BERT, achieved impressive results in semantic processing tasks, their fine-tuning stage still requires large scale training resources. Usually, Data Augmentation (DA) techniques can help to deal with low resource settings. In Text Classificatio…
In real scenarios, a multilingual model trained to solve NLP tasks on a set of languages can be required to support new languages over time. Unfortunately, the straightforward retraining on a dataset containing annotated examples for all the languages is both expensive and time-consuming, especially…