Lubis, Nurul (Nara Institute of Science and Technology) | Sakti, Sakriani (Nara Institute of Science and Technology) | Yoshino, Koichiro (Nara Institute of Science and Technology) | Nakamura, Satoshi (Nara Institute of Science and Technology)
An emotionally-competent computer agent could be a valuable assistive technology in performing various affective tasks. For example caring for the elderly, low-cost ubiquitous chat therapy, and providing emotional support in general, by promoting a more positive emotional state through dialogue system interaction. However, despite the increase of interest in this task, existing works face a number of shortcomings: system scalability, restrictive modeling, and weak emphasis on maximizing user emotional experience. In this paper, we build a fully data driven chat-oriented dialogue system that can dynamically mimic affective human interactions by utilizing a neural network architecture. In particular, we propose a sequence-to-sequence response generator that considers the emotional context of the dialogue. An emotion encoder is trained jointly with the entire network to encode and maintain the emotional context throughout the dialogue. The encoded emotion information is then incorporated in the response generation process. We train the network with a dialogue corpus that contains positive-emotion eliciting responses, collected through crowd-sourcing. Objective evaluation shows that incorporation of emotion into the training process helps reduce the perplexity of the generated responses, even when a small dataset is used. Subsequent subjective evaluation shows that the proposed method produces responses that are more natural and likely to elicit a more positive emotion.
Emotion is intrinsic to humans and consequently emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data in platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration) and more. Additionally, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user's emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a strenuous problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on the recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.
"I'm sorry, this is crazy," said Jamie Chung as she scrolled through Instagram on her iPhone. The actress and "What the Chung" blogger was on a panel called "Influencers of the Future: Tastemakers or AI?" at SXSW in Austin back in March and had just learned about the CGI-generated model and musician Miquela Sousa, also known as @lilmiquela. As Chung pulled up Sousa's feed -- filled with designer fit pics and selfies -- so did the rest of the attendees in the audience. "When we had this conversation prior to talk about the topic, I thought, 'There's no way they're going to take over,'" said Chung to her fellow panelists, Finery co-founders Brooklyn Decker and Whitney Casey. "But after looking at this, I do think it's quite possible."
Recognizing facial action units (AUs) from spontaneous facial expressions is still a challenging problem. Most recently, CNNs have shown promise on facial AU recognition. However, the learned CNNs are often overfitted and do not generalize well to unseen subjects due to limited AU-coded training images. We proposed a novel Incremental Boosting CNN (IB-CNN) to integrate boosting into the CNN via an incremental boosting layer that selects discriminative neurons from the lower layer and is incrementally updated on successive mini-batches. In addition, a novel loss function that accounts for errors from both the incremental boosted classifier and individual weak classifiers was proposed to fine-tune the IB-CNN. Experimental results on four benchmark AU databases have demonstrated that the IB-CNN yields significant improvement over the traditional CNN and the boosting CNN without incremental learning, as well as outperforming the state-of-the-art CNN-based methods in AU recognition. The improvement is more impressive for the AUs that have the lowest frequencies in the databases.
He spent two weeks inside a stuffed bear. He lived inside a hollowed-out rock. And now he's ready to transform into a chicken. French artist Abraham Poincheval isn't your conventional artist, and his stunts and performances are redefining the meaning of "immersive" artwork. For his latest oeuvre, simply entitled "Oeuf" (Egg), Poincheval will live inside a glass vivarium, wrapped in an insulating blanket designed by Korean artist Seglui Lee, until 10 eggs he's sitting on are hatched.