Human use language not just to convey information but also to express their inner feelings and mental states. In this work, we adapt the state-of-the-art language generation models to generate affective (emotional) text. We posit a model capable of generating affect-driven and topic-focused sentences without losing grammatical correctness as the affect intensity increases. We propose to incorporate emotion as prior for the probabilistic state-of-the-art text generation model such as GPT-2. The model gives a user the flexibility to control the category and intensity of emotion as well as the topic of the generated text. Previous attempts at modelling fine-grained emotions fall out on grammatical correctness at extreme intensities, but our model is resilient to this and delivers robust results at all intensities. We conduct automated evaluations and human studies to test the performance of our model and provide a detailed comparison of the results with other models. In all evaluations, our model outperforms existing affective text generation models.
Snap Inc.（色拉布公司）；Ecole Polytechnique, IP Paris（巴黎综合理工学院（巴黎理工学院））；Stanford University（斯坦福大学）
In this work, we explore the emotional reactions that real-world images tend to induce by using natural language as the medium to express the rationale behind an affective response to a given visual stimulus. To embark on this journey, we introduce and share with the research community a large-scale dataset that contains emotional reactions and free-form textual explanations for 85,007 publicly available images, analyzed by 6,283 annotators who were asked to indicate and explain how and why they felt in a particular way when observing a specific image, producing a total of 526,749 responses. E…
Framing a news article means to portray the reported event from a specific perspective, e.g., from an economic or a health perspective. Reframing means to change this perspective. Depending on the audience or the submessage, reframing can become necessary to achieve the desired effect on the readers. Reframing is related to adapting style and sentiment, which can be tackled with neural text generation techniques. However, it is more challenging since changing a frame requires rewriting entire sentences rather than single phrases. In this paper, we study how to computationally reframe sentences…
Prefix-tuning is a powerful lightweight technique for adapting a large pre-trained language model to a downstream application. However, it uses the same dataset-level tuned prompt for all examples in the dataset. We extend this idea and propose a dynamic method, Control Prefixes, which allows for the inclusion of conditional input-dependent information, combining the benefits of prompt tuning and controlled generation. The method incorporates attribute-level learnable representations into different layers of a pre-trained transformer, allowing for the generated text to be guided in a particula…
University of Applied Sciences Potsdam（波茨坦应用科学大学）；Urban Futures Institute of University of Applied Sciences Potsdam（波茨坦应用科学大学城市未来研究所）
This paper presents Emosaic, a tool for visualizing the emotional tone of text documents, considering multiple dimensions of emotion and varying levels of semantic granularity. Emosaic is grounded in psychological research on the relationship between language, affect, and color perception. We capitalize on an established three-dimensional model of human emotion: valence (good, nice vs. bad, awful), arousal (calm, passive vs. exciting, active) and dominance (weak, controlled vs. strong, in control). Previously, multi-dimensional models of emotion have been used rarely in visualizations of textu…
PsychoAgent: An Affect-Sensitive Cognitive Architecture for Conflict-Aware Memory in LLM Agents
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院）；Sharif University of Technology（谢里夫理工大学）；Missouri University of Science and Technology（密苏里科技大学）；Tehran Institute for Advanced Studies, Khatam University（哈塔姆大学德黑兰高等研究院）
The majority of current systems for end-to-end dialog generation focus on response quality without an explicit control over the affective content of the responses. In this paper, we present an affect-driven dialog system, which generates emotional responses in a controlled manner using a continuous representation of emotions. The system achieves this by modeling emotions at a word and sequence level using: (1) a vector representation of the desired emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an affect sampling method, which forces the neural network to generate d…
Toyota Technological Institute at Chicago（芝加哥丰田工学院）
Most prior work on exemplar-based syntactically controlled paraphrase generation relies on automatically-constructed large-scale paraphrase datasets, which are costly to create. We sidestep this prerequisite by adapting models from prior work to be able to learn solely from bilingual text (bitext). Despite only using bitext for training, and in near zero-shot conditions, our single proposed model can perform four tasks: controlled paraphrase generation in both languages and controlled machine translation in both language directions. To evaluate these tasks quantitatively, we create three novel…
AI shares emotion with humans across languages and cultures
University of Science and Technology of China（中国科学技术大学）；Peking University（北京大学）；Tsinghua University（清华大学）；Beijing Normal University（北京师范大学）；The Hong Kong Polytechnic University（香港理工大学）；The PolyU-Hangzhou Technology and Innovation Research Institute（香港理工大学杭州科技创新研究院）；Changping Laboratory（昌平实验室）
The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation
Graduate Institute of Communication Engineering, National Taiwan University, Taiwan（国立台湾大学通讯工程研究所）；Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, USA（美国南加州大学明希电气与计算机工程系）；Gilbert AI Lab, USA（美国Gilbert人工智能实验室）；Graduate Institute of Electrical Engineering, National Taiwan University, Taiwan（国立台湾大学电气工程研究所）；Artificial Intelligence Center of Research Excellence, National Taiwan University, Taiwan（国立台湾大学人工智能研究中心）
