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

Affective resonance in response to others' emotional faces varies with affective ratings and psychopathic traits in amygdala and anterior insula

Despite extensive research on the neural basis of empathic responses for pain and disgust, there is limited data about the brain regions that underpin affective response to other people's emotional facial expressions. Here, we addressed this question using event-related functional magnetic resonance imaging to assess neural responses to emotional faces, combined with online ratings of subjective state. When instructed to rate their own affective response to others' faces, participants recruited anterior insula, dorsal anterior cingulate, inferior frontal gyrus, and amygdala, regions consistently implicated in studies investigating empathy for disgust and pain, as well as emotional saliency. Importantly, responses in anterior insula and amygdala were modulated by trial-by-trial variations in subjective affective responses to the emotional facial stimuli. Furthermore, overall task-elicited activations in these regions were negatively associated with psychopathic personality traits, which are characterized by low affective empathy. Our findings suggest that anterior insula and amygdala play important roles in the generation of affective internal states in response to others' emotional cues and that attenuated function in these regions may underlie reduced empathy in individuals with high levels of psychopathic traits.This work was supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciencia e Tecnologia) under grant number [SFRH/BD/60279/2009] awarded to A.S.C.; the Economic and Social Research Council under grant number [RES-062-23-2202] award to E.V; E.V. is a Royal Society Wolfson Research Merit Award holder; C.L.S. was partially supported during the writing of this article by an Economic and Social Research Council award [ES/K008951/1]; J.P.R. is funded by the Wellcome Trust

Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory

Perception and expression of emotion are key factors to the success of dialogue systems or conversational agents. However, this problem has not been studied in large-scale conversation generation so far. In this paper, we propose Emotional Chatting Machine (ECM) that can generate appropriate responses not only in content (relevant and grammatical) but also in emotion (emotionally consistent). To the best of our knowledge, this is the first work that addresses the emotion factor in large-scale conversation generation. ECM addresses the factor using three new mechanisms that respectively (1) models the high-level abstraction of emotion expressions by embedding emotion categories, (2) captures the change of implicit internal emotion states, and (3) uses explicit emotion expressions with an external emotion vocabulary. Experiments show that the proposed model can generate responses appropriate not only in content but also in emotion.Comment: Accepted in AAAI 201

Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities

Influence of the Cortical Midline Structures on Moral Emotion and Motivation in Moral Decision-Making

The present study aims to examine the relationship between the cortical midline structures (CMS), which have been regarded to be associated with selfhood, and moral decision making processes at the neural level. Traditional moral psychological studies have suggested the role of moral self as the moderator of moral cognition, so activity of moral self would present at the neural level. The present study examined the interaction between the CMS and other moral-related regions by conducting psycho-physiological interaction analysis of functional images acquired while 16 subjects were solving moral dilemmas. Furthermore, we performed Granger causality analysis to demonstrate the direction of influences between activities in the regions in moral decision-making. We first demonstrate there are significant positive interactions between two central CMS seed regions—i.e., the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC)—and brain regions associated with moral functioning including the cerebellum, brainstem, midbrain, dorsolateral prefrontal cortex, orbitofrontal cortex and anterior insula (AI); on the other hand, the posterior insula (PI) showed significant negative interaction with the seed regions. Second, several significant Granger causality was found from CMS to insula regions particularly under the moral-personal condition. Furthermore, significant dominant influence from the AI to PI was reported. Moral psychological implications of these findings are discussed. The present study demonstrated the significant interaction and influence between the CMS and morality-related regions while subject were solving moral dilemmas. Given that, activity in the CMS is significantly involved in human moral functioning

Affect-LM: A Neural Language Model for Customizable Affective Text Generation

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 affect-discriminative word representations, and perplexity experiments show that additional affective information in conversational text can improve language model prediction

Failure to regulate: counterproductive recruitment of top-down prefrontal-subcortical circuitry in major depression

Although depressed mood is a normal occurrence in response to adversity in all individuals, what distinguishes those who are vulnerable to major depressive disorder (MDD) is their inability to effectively regulate negative mood when it arises. Investigating the neural underpinnings of adaptive emotion regulation and the extent to which such processes are compromised in MDD may be helpful in understanding the pathophysiology of depression. We report results from a functional magnetic resonance imaging study demonstrating left-lateralized activation in the prefrontal cortex (PFC) when downregulating negative affect in nondepressed individuals, whereas depressed individuals showed bilateral PFC activation. Furthermore, during an effortful affective reappraisal task, nondepressed individuals showed an inverse relationship between activation in left ventrolateral PFC and the amygdala that is mediated by the ventromedial PFC (VMPFC). No such relationship was found for depressed individuals, who instead show a positive association between VMPFC and amygdala. Pupil dilation data suggest that those depressed patients who expend more effort to reappraise negative stimuli are characterized by accentuated activation in the amygdala, insula, and thalamus, whereas nondepressed individuals exhibit the opposite pattern. These findings indicate that a key feature underlying the pathophysiology of major depression is the counterproductive engagement of right prefrontal cortex and the lack of engagement of left lateral-ventromedial prefrontal circuitry important for the downregulation of amygdala responses to negative stimuli