  Due to its expressivity, natural language is paramount for explicit and
implicit affective state communication among humans. The same linguistic
inquiry (e.g., How are you?) might induce responses with different affects
depending on the affective state of the conversational partner(s) and the
context of the conversation. Yet, most dialog systems do not consider affect as
constitutive aspect of response generation. In this paper, we introduce
AffectON, an approach for generating affective responses during inference. For
generating language in a targeted affect, our approach leverages a
probabilistic language model and an affective space. AffectON is language model
agnostic, since it can work with probabilities generated by any language model
(e.g., sequence-to-sequence models, neural language models, n-grams). Hence, it
can be employed for both affective dialog and affective language generation. We
experimented with affective dialog generation and evaluated the generated text
objectively and subjectively. For the subjective part of the evaluation, we
designed a custom user interface for rating and provided recommendations for
the design of such interfaces. The results, both subjective and objective
demonstrate that our approach is successful in pulling the generated language
toward the targeted affect, with little sacrifice in syntactic coherence.
