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.

Ex Input:
In this paper we present an Action Language-Answer Set Programming based approach to solving planning and scheduling problems in hybrid domains domains that exhibit both discrete and continuous behavior. We use action language H to represent the domain and then translate the resulting theory into an A-Prolog program. In this way, we reduce the problem of finding solutions to planning and scheduling problems to computing answer sets of A-Prolog programs. We cite a planning and scheduling example from the literature and show how to model it in H. We show how to translate the resulting H theory into an equivalent A-Prolog program. We compute the answer sets of the resulting program using a hybrid solver called EZCSP which loosely integrates a constraint solver with an answer set solver. The solver allows us reason about constraints over reals and compute solutions to complex planning and scheduling problems. Results have shown that our approach can be applied to any planning and scheduling problem in hybrid domains.

Ex Output:
Planning and Scheduling in Hybrid Domains Using Answer Set Programming


Ex Input:
The ubiquity of metaphor in our everyday communication makes it an important problem for natural language understanding. Yet, the majority of metaphor processing systems to date rely on handengineered features and there is still no consensus in the field as to which features are optimal for this task. In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Ex Output:
Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection


Ex Input:
Structured learning has found many applications in computer vision recently. Analogues to structured support vector machines (SSVM), here we propose boosting algorithms for predicting multivariate or structured outputs, which is referred to as StructBoost. As SSVM generalizes SVM, our StructBoost generalizes standard boosting such as AdaBoost, or LPBoost to structured learning. AdaBoost, LPBoost and many other conventional boosting methods arise as special cases of StructBoost. The resulting optimization problem of StructBoost is more challenging than SSVM in the sense that the problem of StructBoost can involve exponentially many variables and constraints. In contrast, for SSVM one usually has an exponential number of constraints and a cutting-plane method is used. In order to efficiently solve StructBoost, we propose an equivalent 1-slack formulation and solve it using a combination of cutting planes and column generation. We show the versatility and usefulness of StructBoost on a few problems such as hierarchical multi-class classification, robust visual tracking and image segmentation. In particular, we train a tracking-by-detection based object tracker using the proposed structured boosting. Tracking is implemented as structured output prediction by maximizing the Pascal image area overlap criterion. We show that the structural tracker not only significantly outperforms conventional classification based trackers that do not directly optimize the Pascal image overlap criterion, but also outperforms many other state-of-the-art trackers on the tested videos.

Ex Output:

StructBoost: Boosting Methods for Predicting Structured Output Variables