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Attention mechanism advances the neural machine translation (NMT) by reducing the confusion introduced by irrelevant words in long sentences. However, the confusion caused by ambiguous words hasn't been handled yet and it may be a bottleneck for the NMT model. This paper validates the hypothesis and proposes a simple and flexible framework, which enables the NMT model to only focus on the relevant...
Short text clustering is a challenging problem due to its sparseness of text representation. Here we propose a flexible Self-Taught Convolutional neural network framework for Short Text Clustering (dubbed STC2), which can flexibly and successfully incorporate more useful semantic features and learn non-biased deep text representation in an unsupervised manner. In our framework, the original raw text...
In this paper, we target improving the accuracy of acoustic modelling for statistical parametric speech synthesis (SPSS) and introduce the convolutional neural network (CNN) due to its powerful capacity in locality modelling. A novel model architecture combining unidirectional long short-term memory (LSTM) and a time-domain convolutional output layer (COL) is proposed and employed to acoustic modelling...
In this paper, we create a virtual character that interacts with human player in a sword-fighting task. The data taken from a human player waving a stick as the 'sword' is mapped into the virtual environment, and the result of the collision detection during the virtual interaction is used as a reward for the reinforcement learning. We train a Q-network that animates the virtual character by rotating...
In automatic speech recognition (ASR), connectionist temporal classification (CTC) is regarded as a method to achieve end-to-end system. Actually, not only characters (Chars) but also context independent phonemes (CI-Phns) or context dependent phoneme (CD-Phns) can be used as output units of CTC-trained neural network. The contribution of this paper mainly lies in three aspects: First, we trained...
In this paper, we propose a convolutional framework for short texts expansion and classification. Particularly, by using additive composition over word embeddings from context with variable window width, the representations of multi-scale semantic units are computed first. Empirically, the semantically related words are usually close to each other in embedding spaces. Thus, the restricted nearest...
In actual systems, actuator saturation is a common phenomenon, which often severely restricts system dynamic performance and gives rise to instability. In order to reduce the effects of saturation, this paper presents an adaptive control method based on neural networks (NN) for a class of uncertain nonlinear systems with Brunovsky canonical form and input saturation. This controller is composed of...
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