However, a new algorithm from researchers at Stanford and Adobe has shown it's pretty damn good at video dialogue editing, something that requires artistry, skill and considerable time. For instance, many scenes start with a wide "establishing" shot so that the viewer knows where they are. You can also use leisurely or fast pacing, emphasize a certain character, intensify emotions or keep shot types (like wide or closeup) consistent. In an example shown (below), the team selected "start wide" to establish the scene, "avoid jump cuts" for a cinematic (non-YouTube) style, "emphasize character" ("Stacey") and use a faster-paced performance.
The strategies for interactive characters to select appropriate dialogues remain as an open issue in related research areas. In this paper we propose an approach based on reinforcement learning to learn the strategy of interrogation dialogue from one virtual agent toward another. The emotion variation of the suspect agent is modeled with a hazard function, and the detective agent must learn its interrogation strategies based on the emotion state of the suspect agent. The reinforcement learning reward schemes are evaluated to choose the proper reward in the dialogue.
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.
There are a couple routes screenwriters have for delivering information to their audience. There's exposition -- where plot or character is explained through dialogue -- then there are visual or aural descriptions meant to convey emotion. Basically it all boils down to telling or showing, but however its done, every line of dialogue, every scene, and every emotion in a film should have one express purpose: to advance the story. How a screenwriter chooses to reveal the information of their characters and plot has a direct relation to how we experience the tone and atmosphere of a film, and as such, that information (and our emotions about said information) are ripe for manipulation. Take Alex Garland's script for Ex Machina, which he also directed.
Joshi, Aditya (Monash Research Academy) | Tripathi, Vaibhav (Indian Institute of Technology Bombay) | Soni, Ravindra (Indian Institute of Technology Bombay) | Bhattacharyya, Pushpak (Indian Institute of Technology Bombay) | Carman, Mark James (Monash University)
In this paper, we present an open-source emotion tracker and its innovative applications. Our tracker, EmoGram, tracks emotion changes for a sequence of textual units. It is versatile in terms of the textual unit (tweets, sentences in discourse, etc.) and also what constitutes the time sequence (timestamps of tweets, discourse nature of text, etc.). We demonstrate the utility of our system through our applications: a sequence of commentaries in cricket matches, a sequence of dialogues in a play, and a sequence of tweets related to the Maggi controversy in India in 2015. That one system can be used for these applications is the merit of EmoGram.