TASK DEFINITION: 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.
PROBLEM: We propose a framework grounded in Logic Programming for representing and reasoning about business processes from both the procedural and ontological point of views. In particular, our goal is threefold: (1) define a logical language and a formal semantics for process models enriched with ontology-based annotations; (2) provide an effective inference mechanism that supports the combination of reasoning services dealing with the structural definition of a process model, its behavior, and the domain knowledge related to the participating business entities; (3) implement such a theoretical framework into a process modeling and reasoning platform. To this end we define a process ontology coping with a relevant fragment of the popular BPMN modeling notation. The behavioral semantics of a process is defined as a state transition system by following an approach similar to the Fluent Calculus, and allows us to specify state change in terms of preconditions and effects of the enactment of activities. Then we show how the procedural process knowledge can be seamlessly integrated with the domain knowledge specified by using the OWL 2 RL rule-based ontology language. Our framework provides a wide range of reasoning services, including CTL model checking, which can be performed by using standard Logic Programming inference engines through a goal-oriented, efficient, sound and complete evaluation procedure. We also present a software environment implementing the proposed framework, and we report on an experimental evaluation of the system, whose results are encouraging and show the viability of the approach.

SOLUTION: Ontology-based Representation and Reasoning on Process Models: A Logic Programming Approach

PROBLEM: 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.

SOLUTION: Adversarial Deep Averaging Networks for Cross-Lingual Sentiment Classification

PROBLEM: In this paper we consider the problem of multi-task learning, in which a learner is given a collection of prediction tasks that need to be solved. In contrast to previous work, we give up on the assumption that labeled training data is available for all tasks. Instead, we propose an active task selection framework, where based only on the unlabeled data, the learner can choose a, typically small, subset of tasks for which he gets some labeled examples. For the remaining tasks, which have no available annotation, solutions are found by transferring information from the selected tasks. We analyze two transfer strategies and develop generalization bounds for each of them. Based on this theoretical analysis we propose two algorithms for making the choice of labeled tasks in a principled way and show their effectiveness on synthetic and real data.

SOLUTION:
Active Task Selection for Multi-Task Learning