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.

Example Input: Recurrent neural networks scale poorly due to the intrinsic difficulty in parallelizing their state computations. For instance, the forward pass computation of ht is blocked until the entire computation of ht−1 finishes, which is a major bottleneck for parallel computing. In this work, we propose an alternative RNN implementation by deliberately simplifying the state computation and exposing more parallelism. The proposed recurrent unit operates as fast as a convolutional layer and 5-10x faster than cuDNN-optimized LSTM. We demonstrate the unit’s effectiveness across a wide range of applications including classification, question answering, language modeling, translation and speech recognition. We open source our implementation in PyTorch and CNTK1.
Example Output: Training RNNs as Fast as CNNs

Example Input: We describe how to use propositional model counting for a quantitative analysis of product configuration data. Our approach computes valuable meta information such as the total number of valid configurations or the relative frequency of components. This information can be used to assess the severity of documentation errors or to measure documentation quality. As an application example we show how we apply these methods to product documentation formulas of the Mercedes-Benz line of vehicles. In order to process these large formulas we developed and implemented a new model counter for non-CNF formulas. Our model counter can process formulas, whose CNF representations could not be processed up till now.
Example Output: Model Counting in Product Configuration

Example Input: Named Entity Recognition (NER) models for language L are typically trained using annotated data in that language. We study cross-lingual NER, where a model for NER in L is trained on a source language (or multiple source languages). We introduce a language independent method for NER, building on cross-lingual wikification, a technique that grounds words and phrases in a non-English text into English Wikipedia entries. Thus, mentions in text in any language can be described using a set of categories and FreeBase types, yielding, as we show, strong languageindependent features. With this insight, we propose an NER model that can be applied to all languages in Wikipedia. When trained on English, our model outperforms comparable approaches on the standard CoNLL datasets (Spanish, German, and Dutch) and also performs very well on low-resource languages (Turkish, Tagalog, Yoruba, Bengali, and Tamil) that have significantly smaller Wikipedia. Moreover, our methods allows us to train on multiple source languages, typically improving NER results on the target languages. Finally, we show that our languageindependent features can be used also to enhance monolingual NER systems, yielding improved results for all 9 languages.
Example Output:
Cross-Lingual Named Entity Recognition via Wikification