In 2017, Vaswani et al. proposed a new neural network architecture named Transformer. That modern architecture quickly revolutionized the natural language processing world. Models like GPT and BERT relying on this Transformer architecture have fully outperformed the previous state-of-theart networks. It surpassed the earlier approaches by such a wide margin that all the recent cutting edge models seem to rely on these Transformer-based architectures. In this paper, we provide an overview and explanations of the latest models. We cover the auto-regressive models such as GPT, GPT-2 and XLNET, as well as the auto-encoder architecture such as BERT and a lot of post-BERT models like RoBERTa, ALBERT, ERNIE 1.0/2.0.
Automation in driving will change the role of the drivers from actor to passive supervisor. Although the vehicle will be responsible for driving manoeuvres, drivers will need to rely on automation and understand its decisions to establish a trusting relationship between them and the vehicle. Progress has been made in conversational agents and afective machines recently. Moreover, it seems to be promising in this establishment of trust between humans and machines. We believe it is essential to investigate the use of emotional conversational agents in the automotive context to build a solid relationship between the driver and the vehicle. In this workshop, we aim at gathering researchers and industry practitioners from diferent felds of HCI, ML/AI, NLU and psychology to brainstorm about afective machines, empathy and conversational agent with a particular focus on human-vehicle interaction. Questions like "What would be the specifcities of a multimodal and empathic agent in a car?", "How the agent could make the driver aware of the situation?" and "How to measure the trust between the user and the autonomous vehicle?" will be addressed in this workshop.
CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI).
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