5,905 research outputs found
An Affect-Rich Neural Conversational Model with Biased Attention and Weighted Cross-Entropy Loss
Affect conveys important implicit information in human communication. Having
the capability to correctly express affect during human-machine conversations
is one of the major milestones in artificial intelligence. In recent years,
extensive research on open-domain neural conversational models has been
conducted. However, embedding affect into such models is still under explored.
In this paper, we propose an end-to-end affect-rich open-domain neural
conversational model that produces responses not only appropriate in syntax and
semantics, but also with rich affect. Our model extends the Seq2Seq model and
adopts VAD (Valence, Arousal and Dominance) affective notations to embed each
word with affects. In addition, our model considers the effect of negators and
intensifiers via a novel affective attention mechanism, which biases attention
towards affect-rich words in input sentences. Lastly, we train our model with
an affect-incorporated objective function to encourage the generation of
affect-rich words in the output responses. Evaluations based on both perplexity
and human evaluations show that our model outperforms the state-of-the-art
baseline model of comparable size in producing natural and affect-rich
responses.Comment: AAAI-1
Two-person neuroscience and naturalistic social communication: The role of language and linguistic variables in brain-coupling research
Social cognitive neuroscience (SCN) seeks to understand the brain mechanisms through which we comprehend others? emotions and intentions in order to react accordingly. For decades, SCN has explored relevant domains by exposing individual participants to predesigned stimuli and asking them to judge their social (e.g., emotional) content. Subjects are thus reduced to detached observers of situations that they play no active role in. However, the core of our social experience is construed through real-time interactions requiring the active negotiation of information with other people. To gain more relevant insights into the workings of the social brain, the incipient field of two-person neuroscience (2PN) advocates the study of brain-to-brain coupling through multi-participant experiments. In this paper, we argue that the study of online language-based communication constitutes a cornerstone of 2PN. First, we review preliminary evidence illustrating how verbal interaction may shed light on the social brain. Second, we advance methodological recommendations to design experiments within language-based 2PN. Finally, we formulate outstanding questions for future research.Fil: García, Adolfo Martín. Universidad Nacional de Córdoba. Facultad de Lenguas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Diego Portales; ChileFil: Ibanez Barassi, Agustin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Neurociencia Cognitiva. Fundación Favaloro. Instituto de Neurociencia Cognitiva; Argentina. Universidad Diego Portales; Chile. Universidad Autónoma del Caribe; Colombia. Australian Research Council Centre of Excellence in Cognition and its Disorders; Australi
- …