In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes goldstandard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.

A: Modelling Protagonist Goals and Desires in First-Person Narrative
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Q: Kinect skeleton tracker is able to achieve considerable human body tracking performance in convenient and a low-cost manner. However, The tracker often captures unnatural human poses such as discontinuous and vibrated motions when self-occlusions occur. A majority of approaches tackle this problem by using multiple Kinect sensors in a workspace. Combination of the measurements from different sensors is then conducted in Kalman filter framework or optimization problem is formulated for sensor fusion. However, these methods usually require heuristics to measure reliability of measurements observed from each Kinect sensor. In this paper, we developed a method to improve Kinect skeleton using single Kinect sensor, in which supervised learning technique was employed to correct unnatural tracking motions. Specifically, deep recurrent neural networks were used for improving joint positions and velocities of Kinect skeleton, and three methods were proposed to integrate the refined positions and velocities for further enhancement. Moreover, we suggested a novel measure to evaluate naturalness of captured motions. We evaluated the proposed approach by comparison with the ground truth obtained using a commercial optical maker-based motion capture system.

A: Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks
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Q: Modelling problems containing a mixture of Boolean and numerical variables<lb>is a long-standing interest of Artificial Intelligence. However, performing<lb>inference and learning in hybrid domains is a particularly daunting task.<lb>The ability to model this kind of domains is crucial in “learning to design”<lb>tasks, that is, learning applications where the goal is to learn from examples<lb>how to perform automatic de novo design of novel objects. In this paper we<lb>present Structured Learning Modulo Theories, a max-margin approach for<lb>learning in hybrid domains based on Satisfiability Modulo Theories, which<lb>allows to combine Boolean reasoning and optimization over continuous linear<lb>arithmetical constraints. We validate our method on artificial and real world<lb>scenarios.<lb>

A:
Structured Learning Modulo Theories
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