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]: In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains 43.9/47.7% WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with 15.7% and 5.3% relative improvement respectively.
[A]: HIGHWAY LONG SHORT-TERM MEMORY RNNS FOR DISTANT SPEECH RECOGNITION


[Q]: The rise of smart applications has drawn interest to logical reasoning over data streams. Recently, different query languages and stream processing/reasoning engines were proposed in different communities. However, due to a lack of theoretical foundations, the expressivity and semantics of these diverse approaches are given only informally. Towards clear specifications and means for analytic study, a formal framework is needed to define their semantics in precise terms. To this end, we present a first step towards an ideal semantics that allows for exact descriptions and comparisons of stream reasoning systems.
[A]: Towards Ideal Semantics for Analyzing Stream Reasoning


[Q]: We propose a technique to augment network layers by adding a linear gating mechanism, which provides a way to learn identity mappings by optimizing only one parameter. We also introduce a new metric which served as basis for the technique. It captures the difficulty involved in learning identity mappings for different types of network models, and provides a new theoretical intuition for the increased depths of models such as Highway and Residual Networks. We propose a new model, the Gated Residual Network, which is the result when augmenting Residual Networks. Experimental results show that augmenting layers grants increased performance, less issues with depth, and more layer independence – fully removing them does not cripple the model. We evaluate our method on MNIST using fully-connected networks and on CIFAR-10 using Wide ResNets, achieving a relative error reduction of more than 8% in the latter when compared to the original model.
[A]:
LEARNING IDENTITY MAPPINGS WITH RESIDUAL GATES