Developing Deployable Spoken Language Translation Systems given Limited Resources

Approaches are presented that support the deployment of spoken language translation systems. Newly developed methods allow low cost portability to new language pairs. Proposed translation model pruning techniques achieve a high translation performance even in low memory situations. The named entity and specialty vocabulary coverage, particularly on small and mobile devices, is targeted to an individual user by translation model personalization

Non-linear Learning for Statistical Machine Translation

Modern statistical machine translation (SMT) systems usually use a linear combination of features to model the quality of each translation hypothesis. The linear combination assumes that all the features are in a linear relationship and constrains that each feature interacts with the rest features in an linear manner, which might limit the expressive power of the model and lead to a under-fit model on the current data. In this paper, we propose a non-linear modeling for the quality of translation hypotheses based on neural networks, which allows more complex interaction between features. A learning framework is presented for training the non-linear models. We also discuss possible heuristics in designing the network structure which may improve the non-linear learning performance. Experimental results show that with the basic features of a hierarchical phrase-based machine translation system, our method produce translations that are better than a linear model.Comment: submitted to a conferenc

DESQ: Frequent Sequence Mining with Subsequence Constraints

Frequent sequence mining methods often make use of constraints to control which subsequences should be mined. A variety of such subsequence constraints has been studied in the literature, including length, gap, span, regular-expression, and hierarchy constraints. In this paper, we show that many subsequence constraints---including and beyond those considered in the literature---can be unified in a single framework. A unified treatment allows researchers to study jointly many types of subsequence constraints (instead of each one individually) and helps to improve usability of pattern mining systems for practitioners. In more detail, we propose a set of simple and intuitive "pattern expressions" to describe subsequence constraints and explore algorithms for efficiently mining frequent subsequences under such general constraints. Our algorithms translate pattern expressions to compressed finite state transducers, which we use as computational model, and simulate these transducers in a way suitable for frequent sequence mining. Our experimental study on real-world datasets indicates that our algorithms---although more general---are competitive to existing state-of-the-art algorithms.Comment: Long version of the paper accepted at the IEEE ICDM 2016 conferenc

Light Multi-segment Activation for Model Compression

Model compression has become necessary when applying neural networks (NN) into many real application tasks that can accept slightly-reduced model accuracy with strict tolerance to model complexity. Recently, Knowledge Distillation, which distills the knowledge from well-trained and highly complex teacher model into a compact student model, has been widely used for model compression. However, under the strict requirement on the resource cost, it is quite challenging to achieve comparable performance with the teacher model, essentially due to the drastically-reduced expressiveness ability of the compact student model. Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model. Specifically, we propose a highly efficient multi-segment activation, called Light Multi-segment Activation (LMA), which can rapidly produce multiple linear regions with very few parameters by leveraging the statistical information. With using LMA, the compact student model is capable of achieving much better performance effectively and efficiently, than the ReLU-equipped one with same model scale. Furthermore, the proposed method is compatible with other model compression techniques, such as quantization, which means they can be used jointly for better compression performance. Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA

Efficient deep neural network inference for embedded systems:A mixture of experts approach

Deep neural networks (DNNs) have become one of the dominant machine learning approaches in recent years for many application domains. Unfortunately, DNNs are not well suited to addressing the challenges of embedded systems, where on-device inference on battery-powered, resource-constrained devices is often infeasible due to prohibitively long inferencing time and resource requirements. Furthermore, offloading computation into the cloud is often infeasible due to a lack of connectivity, high latency, or privacy concerns. While compression algorithms often succeed in reducing inferencing times, they come at the cost of reduced accuracy. The key insight here is that multiple DNNs, of varying runtimes and prediction capabilities, are capable of correctly making a prediction on the same input. By choosing the fastest capable DNN for each input, the average runtime can be reduced. Furthermore, the fastest capable DNN changes depending on the evaluation criterion. This thesis presents a new, alternative approach to enable efficient execution of DNN inference on embedded devices; the aim is to reduce average DNN inferencing times without a loss in accuracy. Central to the approach is a Model Selector, which dynamically determines which DNN to use for a given input, by considering the desired evaluation metric and inference time. It employs statistical machine learning to develop a low-cost predictive model to quickly select a DNN to use for a given input and the optimisation constraint. First, the approach is shown to work effectively with off-the-self pre-trained DNNs. The approach is then extended by combining typical DNN pruning techniques with statistical machine learning in order to create a set of specialised DNNs designed specifically for use with a Model Selector. Two typical DNN application domains are used during evaluation: image classification and machine translation. Evaluation is reported on a NVIDIA Jetson TX2 embedded deep learning platform, and a range of influential DNN models including convolutional and recurrent neural networks are considered. In the first instance, utilising off-the-shelf pre-trained DNNs, a 44.45% reduction in inference time with a 7.52% improvement in accuracy, over the most-capable single DNN model, is achieved for image classification. For machine translation, inference time is reduced by 25.37% over the most-capable model with little impact on the quality of the translation. Further evaluation utilising specialised DNNs did not yield an accurate premodel and produced poor results; however analysis of a perfect premodel shows the potential for faster inference times, and reduced resource requirements over utilising off-the-shelf DNNs