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Analysing and modelling interactive behaviour is an important topic inhuman-computer interaction (HCI) and a key requirement for the development ofintelligent interactive systems. Interactive behaviour has a sequential(actions happen one after another) and hierarchical (a sequence of actionsforms an activity driven by interaction goals) structure, which may be similarto the structure of natural language. Designed based on such a structure,natural language processing (NLP) methods have achieved groundbreaking successin various downstream tasks. However, few works linked interactive behaviourwith natural language. In this paper, we explore the similarity betweeninteractive behaviour and natural language by applying an NLP method, byte pairencoding (BPE), to encode mouse and keyboard behaviour. We then analyse thevocabulary, i.e., the set of action sequences, learnt by BPE, as well as usethe vocabulary to encode the input behaviour for interactive task recognition.An existing dataset collected in constrained lab settings and our novelout-of-the-lab dataset were used for evaluation. Results show that this naturallanguage-inspired approach not only learns action sequences that reflectspecific interaction goals, but also achieves higher F1 scores on taskrecognition than other methods. Our work reveals the similarity betweeninteractive behaviour and natural language, and presents the potential ofapplying the new pack of methods that leverage insights from NLP to modelinteractive behaviour in HCI.

Exploring Natural Language Processing Methods for Interactive Behaviour Modelling