Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016;Gulordava et al., 2018) and neural machine translation (Shi et al., 2016).In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017;Vaswani et al., 2017).In this work, we compare the two architectures-recurrent versus non-recurrent-with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose.The code and data used in our experiments is available at https://github.com/ ketranm/fan_vs_rnn

We introduce a manually-created, multireference dataset for abstractive sentence and short paragraph compression.First, we examine the impact of single-and multi-sentence level editing operations on human compression quality as found in this corpus.We observe that substitution and rephrasing operations are more meaning preserving than other operations, and that compressing in context improves quality.Second, we systematically explore the correlations between automatic evaluation metrics and human judgments of meaning preservation and grammaticality in the compression task, and analyze the impact of the linguistic units used and precision versus recall measures on the quality of the metrics.Multi-reference evaluation metrics are shown to offer significant advantage over single reference-based metrics.

Recurrent Neural Networks (RNNs) have obtained excellent result in many natural language processing (NLP) tasks.However, understanding and interpreting the source of this success remains a challenge.In this paper, we propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only amplifies the power of RNN but also facilitates our understanding of its internal functioning and allows us to discover underlying patterns in data.We demonstrate the power of RMN on language modeling and sentence completion tasks.On language modeling, RMN outperforms Long Short-Term Memory (LSTM) network on three large German, Italian, and English dataset.Additionally we perform indepth analysis of various linguistic dimensions that RMN captures.On Sentence Completion Challenge, for which it is essential to capture sentence coherence, our RMN obtains 69.2% accuracy, surpassing the previous state of the art by a large margin. 1

Translating into morphologically rich lan-guages is a particularly difficult problem in machine translation due to the high de-gree of inflectional ambiguity in the tar-get language, often only poorly captured by existing word translation models. We present a general approach that exploits source-side contexts of foreign words to improve translation prediction accuracy. Our approach is based on a probabilistic neural network which does not require lin-guistic annotation nor manual feature en-gineering. We report significant improve-ments in word translation prediction accu-racy for three morphologically rich target languages. In addition, preliminary results for integrating our approach into a large-scale English-Russian statistical machine translation system show small but statisti-cally significant improvements in transla-tion quality. 1