Diyalog oluşturmada duyguyu dahil etme

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, despite few studies which perceive affect as constitutive aspect of response generation, most of the existing dialog systems produce identical responses to a given inquiry, irrespective of affective information. In this thesis, we introduce AffectON, an approach for integrating affect into dialog generation. For generating language in a targeted affect, our approach leverages a probabilistic language model, an affective space and word embeddings. AffectON is language model agnostic since the probabilistic distribution can originate from any probabilistic language model (i.e. sequence-to-sequence models, neural language models, n-grams). Hence, it can be employed for both affective dialog and affective language generation. We experiment with affective dialog generation and conduct subjective and objective evaluations. For the subjective part of the evaluation, we design a designated user interface for rating and provide recommendations for the design of such interfaces. The results, both subjective and objective show that our approach is successful in pulling the generated language toward the targeted affect, with little sacrifice in syntactic coherence.