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.

In recent years deep neural networks have achieved great success in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most other languages do not enjoy such an abundance of annotated data for sentiment analysis. To combat this problem, we propose the Adversarial Deep Averaging Network (ADAN) to transfer sentiment knowledge learned from labeled English data to lowresource languages where only unlabeled data exists. ADAN is a “Y-shaped” network with two discriminative branches: a sentiment classifier and an adversarial language predictor. Both branches take input from a feature extractor that aims to learn hidden representations that capture the underlying sentiment of the text and are invariant across languages. Experiments on Chinese sentiment classification demonstrate that ADAN significantly outperforms several baselines, including a strong pipeline approach that relies on Google Translate, the state-of-the-art commercial machine translation system.
Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

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.
Tracking Human-like Natural Motion Using Deep Recurrent Neural Networks

We describe methods for managing the com­ plexity of information displayed to people responsible for making high-stakes, time­ critical decisions. The techniques provide tools for real-time control of the configura­ tion and quantity of information displayed to a user, and a methodology for designing flexible human-computer interfaces for mon­ itoring applications. After defining a proto­ typical set of display decision problems, we introduce the expected value of revealed in­ formation (EVRI) and the related measure of expected value of displayed information (EVDI) . We describe how these measures can be used to enhance computer displays used for monitoring complex systems. We moti­ vate the presentation by discussing our ef­ forts to employ decision-theoretic control of displays for a time-critical monitoring appli­ cation at the NASA Mission Control Center in Houston.

Display of Information for Time-Critical Decision Making