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.

This paper investigates neural characterbased morphological tagging for languages with complex morphology and large tag sets. We systematically explore a variety of neural architectures (DNN, CNN, CNNHighway, LSTM, BLSTM) to obtain character-based word vectors combined with bidirectional LSTMs to model across-word context in an end-to-end setting. We explore supplementary use of word-based vectors trained on large amounts of unlabeled data. Our experiments for morphological tagging suggest that for ”simple” model configurations, the choice of the network architecture (CNN vs. CNNHighway vs. LSTM vs. BLSTM) or the augmentation with pre-trained word embeddings can be important and clearly impact the accuracy. Increasing the model capacity by adding depth, for example, and carefully optimizing the neural networks can lead to substantial improvements, and the differences in accuracy (but not training time) become much smaller or even negligible. Overall, our best morphological taggers for German and Czech outperform the best results reported in the literature by a large margin.
Neural Morphological Tagging from Characters for Morphologically Rich Languages

For most reinforcement learning approaches, the learning is performed by maximizing an accumulative reward that is expectedly and manually defined for specific tasks. However, in real world, rewards are emergent phenomena from the complex interactions between agents and environments. In this paper, we propose an implicit generic reward model for reinforcement learning. Unlike those rewards that are manually defined for specific tasks, such implicit reward is task independent. It only comes from the deviation from the agents’ previous experiences.
Experience enrichment based task independent reward model

How can we automatically discover the most important correspondences between words from two or more languages? How can we do so allowing for correspondences between any subset of languages, without drowning in redundant results, and at the same time maintaining control over the level of detail? These are exactly the questions we answer in this paper. We approach the problem with the Minimum Description Length principle, and give an efficient algorithm for discovering statistically important correspondences. We test the efficacy of our method against a set of Slavic languages. The experiments show our method automatically discovers non-trivial associations, allowing for both quantitative and qualitative analysis of multiple languages.

Discovering Correspondences between Multiple Languages by MDL