1,555 research outputs found

    Stochastic Language Generation in Dialogue using Recurrent Neural Networks with Convolutional Sentence Reranking

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    The natural language generation (NLG) component of a spoken dialogue system (SDS) usually needs a substantial amount of handcrafting or a well-labeled dataset to be trained on. These limitations add significantly to development costs and make cross-domain, multi-lingual dialogue systems intractable. Moreover, human languages are context-aware. The most natural response should be directly learned from data rather than depending on predefined syntaxes or rules. This paper presents a statistical language generator based on a joint recurrent and convolutional neural network structure which can be trained on dialogue act-utterance pairs without any semantic alignments or predefined grammar trees. Objective metrics suggest that this new model outperforms previous methods under the same experimental conditions. Results of an evaluation by human judges indicate that it produces not only high quality but linguistically varied utterances which are preferred compared to n-gram and rule-based systems.Comment: To be appear in SigDial 201

    The Modified Matrix Splitting Iteration Method for Computing PageRank Problem

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    In this paper, based on the iteration methods [3,10], we propose a modified multi-step power-inner-outer (MMPIO) iteration method for solving the PageRank problem. In the MMPIO iteration method, we use the multi-step matrix splitting iterations instead of the power method, and combine with the inner-outer iteration [24]. The convergence of the MMPIO iteration method is analyzed in detail, and some comparison results are also given. Several numerical examples are presented to illustrate the effectiveness of the proposed algorithm

    Hybrid meshless/displacement discontinuity method for FGM Reissner's plate with cracks

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    Growing applications of non-homogenous media in engineering structures require the application of powerful computational tools. A novel hybrid Meshless Displacement Discontinuity Method (MDDM) for cracked Reissner's plate in Functionally Graded Materials (FGMs) is presented in this paper. The fundamental solutions of slope and deflection discontinuity for an isotropic homogenous media are chosen as a part of general solutions to create the gaps between the crack surfaces. The governing equation is satisfied by using the meshless methods such as the Meshless Local Petrov-Galerkin (MLPG) and the Point Collocation Method (PCM) with Lagrange series interpolation and mapping technique. The Stress Intensity Factors (SIFs) are evaluated analytically with the Chebyshev polynomials. The accuracy is verified by comparison of numerical and analytical results
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