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

[Q]: Language learning is thought to be a highly complex process. One of the hurdles in learning a language is to learn the rules of syntax of the language. Rules of syntax are often ordered in that before one rule can applied one must apply another. It has been thought that to learn the order of n rules one must go through all n! permutations. Thus to learn the order of 27 rules would require 27! steps or 1.08889x10 steps. This number is much greater than the number of seconds since the beginning of the universe! In an insightful analysis the linguist Block ([Block 86], pp. 62-63, p.238) showed that with the assumption of transitivity this vast number of learning steps reduces to a mere 377 steps. We present a mathematical analysis of the complexity of Block's algorithm. The algorithm has a complexity of order n given n rules. In addition, we improve Block's results exponentially, by introducing an algorithm that has complexity of order less than n log n.
[A]: On the complexity of learning a language: An improvement of Block's algorithm

[Q]: While recent neural machine translation approaches have delivered state-of-the-art performance for resource-rich language pairs, they suffer from the data scarcity problem for resource-scarce language pairs. Although this problem can be alleviated by exploiting a pivot language to bridge the source and target languages, the source-to-pivot and pivot-to-target translation models are usually independently trained. In this work, we introduce a joint training algorithm for pivot-based neural machine translation. We propose three methods to connect the two models and enable them to interact with each other during training. Experiments on Europarl and WMT corpora show that joint training of source-to-pivot and pivot-to-target models leads to significant improvements over independent training across various languages.
[A]:
Joint Training for Pivot-based Neural Machine Translation