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.

Q: We propose a new encoder-decoder approach to learn distributed sentence representations from unlabeled sentences. The word-to-vector representation is used, and convolutional neural networks are employed as sentence encoders, mapping an input sentence into a fixed-length vector. This representation is decoded using long short-term memory recurrent neural networks, considering several tasks, such as reconstructing the input sentence, or predicting the future sentence. We further describe a hierarchical encoder-decoder model to encode a sentence to predict multiple future sentences. By training our models on a large collection of novels, we obtain a highly generic convolutional sentence encoder that performs well in practice. Experimental results on several benchmark datasets, and across a broad range of applications, demonstrate the superiority of the proposed model over competing methods.

A: Unsupervised Learning of Sentence Representations using Convolutional Neural Networks
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Q: We introduce Discriminative BLEU (∆BLEU), a novel metric for intrinsic evaluation of generated text in tasks that admit a diverse range of possible outputs. Reference strings are scored for quality by human raters on a scale of [−1, +1] to weight multi-reference BLEU. In tasks involving generation of conversational responses, ∆BLEU correlates reasonably with human judgments and outperforms sentence-level and IBM BLEU in terms of both Spearman’s ρ and Kendall’s τ .

A: ∆BLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets∗
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Q: We investigate the direct-sum problem in the context of differentially private PAC learning: What<lb>is the sample complexity of solving k learning tasks simultaneously under differential privacy, and how<lb>does this cost compare to that of solving k learning tasks without privacy? In our setting, an individual<lb>example consists of a domain element x labeled by k unknown concepts (c1, . . . ,<lb>ck). The goal of a<lb>multi-learner is to output k hypotheses (h1, . . . , hk) that generalize the input examples.<lb>Without concern for privacy, the sample complexity needed to simultaneously learn k concepts is<lb>essentially the same as needed for learning a single concept. Under differential privacy, the basic strategy<lb>of learning each hypothesis independently yields sample complexity that grows polynomially with k.<lb>For some concept classes, we give multi-learners that require fewer samples than the basic strategy.<lb>Unfortunately, however, we also give lower bounds showing that even for very simple concept classes,<lb>the sample cost of private multi-learning must grow polynomially in k.

A:
Simultaneous Private Learning of Multiple Concepts
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