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 Q]: In many domains it is desirable to assess the pref­ erences of users in a qualitative rather than quan­ titative way. Such representations of qualitative preference orderings form an important compo­ nent of automated decision tools. We propose a graphical representation of preferences that re­ flects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a rep­ resentation is often compact and arguably natural. We describe several search algorithms for domi­ nance testing based on this representation; these algorithms are quite effective, especially in spe­ cific network topologies, such as chainand tree­ structured networks, as well as polytrees.
[EX A]: Reasoning With Conditional Ceteris Paribus Preference Statements

[EX Q]: We present Deep Speaker, a neural speaker embedding system that maps utterances to a hypersphere where speaker similarity is measured by cosine similarity. The embeddings generated by Deep Speaker can be used for many tasks, including speaker identification, verification, and clustering. We experiment with ResCNN and GRU architectures to extract the acoustic features, then mean pool to produce utterance-level speaker embeddings, and train using triplet loss based on cosine similarity. Experiments on three distinct datasets suggest that Deep Speaker outperforms a DNN-based i-vector baseline. For example, Deep Speaker reduces the verification equal error rate by 50% (relatively) and improves the identification accuracy by 60% (relatively) on a text-independent dataset. We also present results that suggest adapting from a model trained with Mandarin can improve accuracy for English speaker recognition.
[EX A]: Deep Speaker: an End-to-End Neural Speaker Embedding System

[EX Q]: Many popular knowledge graphs such as Freebase, YAGO or DBPedia maintain a list of non-discrete aributes for each entity. Intuitively, these aributes 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 aribute information in a relational setting. Specically, we train a neural network for triplet prediction along with a separate network for aribute value regression. Via multi-task learning, we are able to learn representations of entities, relations and aributes that encode information about both tasks. Moreover, such aributes 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 aractive as well. We show that our approach outperforms many state-ofthe-art methods for the tasks of relational triplet classication and aribute value prediction.
[EX A]:
Multi-Task Neural Network for Non-discrete Aribute Prediction in Knowledge Graphs