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.
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Question: Recently, bidirectional recurrent network language models (biRNNLMs) have been shown to outperform standard, unidirectional, recurrent neural network language models (uni-RNNLMs) on a range of speech recognition tasks. This indicates that future word context information beyond the word history can be useful. However, bi-RNNLMs pose a number of challenges as they make use of the complete previous and future word context information. This impacts both training efficiency and their use within a lattice rescoring framework. In this paper these issues are addressed by proposing a novel neural network structure, succeeding word RNNLMs (suRNNLMs). Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a finite number of succeeding, future, words. This model can be trained much more efficiently than bi-RNNLMs and can also be used for lattice rescoring. Experimental results on a meeting transcription task (AMI) show the proposed model consistently outperformed uni-RNNLMs and yield only a slight degradation compared to bi-RNNLMs in N-best rescoring. Additionally, performance improvements can be obtained using lattice rescoring and subsequent confusion network decoding.

Answer: FUTURE WORD CONTEXTS IN NEURAL NETWORK LANGUAGE MODELS

Question: Distributed controllers are oftentimes used in largescale SDN deployments where they run a myriad of network applications simultaneously. Such applications could have different consistency and availability preferences. These controllers need to communicate via east/west interfaces in order to synchronize their state information. The consistency and the availability of the distributed state information are governed by an underlying consistency model. Earlier, we suggested [1] the use of adaptivelyconsistent controllers that can autonomously tune their consistency parameters in order to meet the performance requirements of a certain application. In this paper, we examine the feasibility of employing adaptive controllers that are built on-top of tunable consistency models similar to that of Apache Cassandra. We present an adaptation strategy that uses clustering techniques (sequential k-means and incremental k-means) in order to map a given application performance indicator () into a feasible consistency level () that can be used with the underlying tunable consistency model. In the cases that we modeled and tested, our results show that in the case of sequential k-means, with a reasonable number of clusters ( 50), a plausible mapping (low RMSE) could be estimated between the application performance indicators () and the consistency level indicator (). In the case of incremental k-means, the results also showed that a plausible mapping (low RMSE) could be estimated using a similar number of clusters ( 50) by using a small threshold ( 0.01).

Answer: A Clustering-based Consistency Adaptation Strategy for Distributed SDN Controllers

Question: This work focuses on answering singlerelation factoid questions over Freebase. Each question can acquire the answer from a single fact of form (subject, predicate, object) in Freebase. This task, simple question answering (SimpleQA), can be addressed via a twostep pipeline: entity linking and fact selection. In fact selection, we match the subject entity in fact with the entity mention in question by a character-level convolutional neural network (char-CNN), and match the predicate in fact with the question by a word-level CNN (wordCNN). This work makes two main contributions. (i) A simple and effective entity linker over Freebase is proposed. Our entity linker outperforms the state-of-the-art entity linker of SimpleQA task. (ii) A novel attentive maxpooling is stacked over word-CNN, so that the predicate representation can be matched with the predicate-focused question representation more effectively. Experiments show that our system sets new state-of-the-art in this task.

Answer:
Simple Question Answering by Attentive Convolutional Neural Network