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.
Input: Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete aSributes for each entity. Intuitively, these aSributes such as height, price or population count are able to richly characterize entities in knowledge graphs. is additional source of information may help to alleviate the inherent sparsity and incompleteness problem that are prevalent in knowledge graphs. Unfortunately, many state-of-the-art relational learning models ignore this information due to the challenging nature of dealing with non-discrete data types in the inherently binary-natured knowledge graphs. In this paper, we propose a novel multi-task neural network approach for both encoding and prediction of non-discrete aSribute information in a relational setting. Specically, we train a neural network for triplet prediction along with a separate network for aSribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and aSributes that encode information about both tasks. Moreover, such aSributes are not only central to many predictive tasks as an information source but also as a prediction target. erefore, models that are able to encode, incorporate and predict such information in a relational learning context are highly aSractive as well. We show that our approach outperforms many state-ofthe-art methods for the tasks of relational triplet classication and aSribute value prediction.
Output:
Multi-Task Neural Network for Non-discrete A'ribute Prediction in Knowledge Graphs