Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research in this direction but the problem of integrating state-of-the-art neural language models with affective information remains an area ripe for exploration. In this paper, we propose an extension to an LSTM (Long Short-Term Memory) language model for generating conversational text, conditioned on affect categories. Our proposed model, Affect-LM enables us to customize the degree of emotional content in generated sentences through an additional design parameter. Perception studies conducted using Amazon Mechanical Turk show that Affect-LM generates naturally looking emotional sentences without sacrificing grammatical correctness. Affect-LM also learns affectdiscriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction.

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The TARDIS project aims to build a scenario-based serious-game simulation platform for NEETs and job-inclusion associations that supports social training and coaching in the context of job interviews. This paper presents the general architecture of the TARDIS job interview simulator, and the serious game paradigm that we are developing. 1 NEET is a government acronym for young people not in employment, education or training. 2 ec.europa.eu/eurostat 3

A growing body of evidence shows that virtual audiences are a valuable tool in the treatment of social anxiety, and recent works show that it also a useful in public-speaking training programs. However, little research has focused on how such audiences are perceived and on how the behavior of virtual audiences can be manipulated to create various types of stimuli. The authors used a crowdsourcing methodology to create a virtual audience nonverbal behavior model and, with it, created a dataset of videos with virtual audiences containing varying behaviors. Using this dataset, they investigated how virtual audiences are perceived and which factors affect this perception.