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Found 20 papers, 14 papers with code
Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress.
Large Language Models Produce Responses Perceived to be Empathic
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy.
Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models
The emotions we experience involve complex processes; besides physiological aspects, research in psychology has studied cognitive appraisals where people assess their situations subjectively, according to their own values (Scherer, 2005).
Using Positive Matching Contrastive Loss with Facial Action Units to mitigate bias in Facial Expression Recognition
Machine learning models automatically learn discriminative features from the data, and are therefore susceptible to learn strongly-correlated biases, such as using protected attributes like gender and race.
Facial Expression Recognition
Facial Expression Recognition (FER)
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ReaSCAN: Compositional Reasoning in Language Grounding
The ability to compositionally map language to referents, relations, and actions is an essential component of language understanding.
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
Fine-grained classification involves dealing with datasets with larger number of classes with subtle differences between them.
Using Knowledge-Embedded Attention to Augment Pre-trained Language Models for Fine-Grained Emotion Recognition
Modern emotion recognition systems are trained to recognize only a small set of emotions, and hence fail to capture the broad spectrum of emotions people experience and express in daily life.
An Ethical Framework for Guiding the Development of Affectively-Aware Artificial Intelligence
We propose a multi-stakeholder analysis framework that separates the ethical responsibilities of AI Developers vis-\`a-vis the entities that deploy such AI -- which we term Operators.
Critically examining the Domain Generalizability of Facial Expression Recognition models
Using a state-of-the-art model with twelve datasets (six collected in-lab and six ``in-the-wild"), we conduct extensive round-robin-style experiments to evaluate classification accuracies when given new data from an unseen dataset.
On Explaining Your Explanations of BERT: An Empirical Study with Sequence Classification
In this paper, we adapt existing attribution methods on explaining decision makings of BERT in sequence classification tasks.
Context-Guided BERT for Targeted Aspect-Based Sentiment Analysis
We train both models with pretrained BERT on two (T)ABSA datasets: SentiHood and SemEval-2014 (Task 4).
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
Structured Self-Attention Weights Encode Semantics in Sentiment Analysis
Very recent work suggests that the self-attention in the Transformer encodes syntactic information; Here, we show that self-attention scores encode semantics by considering sentiment analysis tasks.
Pragmatically Informative Color Generation by Grounding Contextual Modifiers
One important task that involves grounding contextual modifiers is color generation.
Improving Multi-Agent Cooperation using Theory of Mind
Recent advances in Artificial Intelligence have produced agents that can beat human world champions at games like Go, Starcraft, and Dota2.
Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset
We begin by assessing the state-of-the-art in time-series emotion recognition, and we review contemporary time-series approaches in affective computing, including discriminative and generative models.
Robot Capability and Intention in Trust-based Decisions across Tasks
In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions.
Attending to Emotional Narratives
Attention mechanisms in deep neural networks have achieved excellent performance on sequence-prediction tasks.
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series
Integrating deep learning with latent state space models has the potential to yield temporal models that are powerful, yet tractable and interpretable.
Applying Probabilistic Programming to Affective Computing
Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models.
A Multimodal LSTM for Predicting Listener Empathic Responses Over Time
People naturally understand the emotions of-and often also empathize with-those around them.
