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Context-extended phrase reordering model for pivot-based statistical machine translation

Context-extended phrase reordering model for pivot-based sta...

引用

International Conference on Asian Language Processing (IALP)

作者： Xiaoning Zhu Tiejun Zhao Yiming Cui Conghui Zhu School of Computer Science and Technology Harbin Institute of Technology Harbin China Harbin Institute of Technology Harbin Heilongjiang CN

For translation between language pairs which is lack of bilingual data, pivot-based SMT uses a pivot language as a “bridge” to generate source-target translation, inducing from source-pivot and pivot-target translation. However, due to the missing of the context information, the reordering model was hard to obtain with the conventional methods. In this paper, we present a context-extended phrase reordering model for pivot-based statistical machine translation by extending the context information in source, pivot and target language. Experimental results show that our method leads to significant improvements over the baseline system on European Parliament data.

关键词： context machine translation pivot reordering machine translation Pivot Device Component REORDERING object oriented programming languages

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LEARNING CROSS-LINGUAL INFORMATION WITH MULTILINGUAL BLSTM FOR SPEECH SYNTHESIS OF LOW-RESOURCE languages

LEARNING CROSS-LINGUAL INFORMATION WITH MULTILINGUAL BLSTM F...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Quanjie Yu Peng Liu Zhiyong Wu Shiyin Kang Helen Meng Lianhong Cai Tsinghua-CUHK Joint Research Center for Media Sciences Technologies and Systems Graduate School at Shenzhen Tsinghua University Shenzhen 518055 China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Shatin N.T. Hong Kong SAR China

ISBN: (纸本)9781479999897

Bidirectional long short-term memory (BLSTM) based speech synthesis has shown great potential in improving the quality of the synthetic speech. However, for low-resource languages, it is difficult to obtain a high quality BLSTM model. BLSTM based speech synthesis can be viewed as a transformation between the input features and the output features. We assume that the input and output layers of BLSTM are language-dependent while the hidden layers can be language-independent if trained properly. We investigate whether sufficient training data of another language (auxiliary) can benefit the BLSTM training of a new language (target) that has only limited training data. In this paper, we propose 1) a multilingual BLSTM that shares hidden layers across different languages and 2) a specific training approach that can best utilize the training data from both the auxiliary and target languages. Experimental results demonstrate the effectiveness of the proposed approach. The multilingual BLSTM can learn the cross-lingual information, and can predict more accurate acoustic features for speech synthesis of the target language than the monolingual BLSTM that is trained with only the data from the target language. Subjective test also indicates that multilingual BLSTM outperforms the monolingual BLSTM in generating higher quality synthetic speech.

关键词： Speech synthesis bidirectional long shortterm memory (BLSTM) low-resource multilingual cross-lingual Speech synthesis object oriented programming languages Language Multilingualism Training data language-independent biological control

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A Telephone Speech Corpus of China’s Minority languages for Automatic Language Identification winter

A Telephone Speech Corpus of China’s Minority languages for...

引用

2013 AASRI Winter International Conference on Engineering and Technology(AASRI-WIET 2013)

作者： Xiuhua Zeng Jian Yang Libo Zuo Yonghua Xu School of Physics and Electronic Engineering Qujing Normal University School of Information Science and Engineering Yunnan University

Research in language identification require corpus of multi-languages speech data to capture the distinguishable information within and across *** the past few decades,many statistical approa

ISBN: (纸本)9781629939827

Research in language identification require corpus of multi-languages speech data to capture the distinguishable information within and across *** the past few decades,many statistical approa

关键词： Language identification Telephone speech Corpus Minority languages Telephone mandarins object oriented programming languages minorities language recognition Language Speech

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MACHINE TRANSLATION BASED DATA AUGMENTATION FOR CANTONESE KEYWORD SPOTTING

MACHINE TRANSLATION BASED DATA AUGMENTATION FOR CANTONESE KE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Guangpu Huang Arseniy Gorin Jean-Luc Gauvain Lori Lamel LIMSI CNRS Spoken Language Processing Group 91405 Orsay Cedex France

ISBN: (纸本)9781479999897

This paper presents a method to improve a language model for a limited-resourced language using statistical machine translation from a related language to generate data for the target language. In this work, the machine translation model is trained on a corpus of parallel Mandarin-Cantonese subtitles and used to translate a large set of Mandarin conversational telephone transcripts to Cantonese, which has limited resources. The translated transcripts are used to train a more robust language model for speech recognition and for keyword search in Cantonese conversational telephone speech. This method enables the keyword search system to detect 1.5 times more out-of-vocabulary words, and achieve 1.7% absolute improvement on actual term-weighted value.

关键词： keyword spotting data augmentation language modelling neural networks low-resourced languages modelling languages machine translation Neural network object oriented programming languages Key Search Cantonese KEYWORD

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Language Transfer of Audio Word2Vec: Learning Audio Segment Representations Without Target Language Data

Language Transfer of Audio Word2Vec: Learning Audio Segment ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chia-Hao Shen Janet Y. Sung Hung-Yi Lee National Taiwan University Electrical Engineering Department Harvard University Design Engineering Department

ISBN: (纸本)9781538646595

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as spoken term detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch the phonetic structure from the audio segments of the target language if the source and target languages are similar. In STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.

关键词： Audio Word2Vec Spoken Term Detection Seq2Seq Autoencoder Language Transfer Audio vector representation object oriented programming languages representations Language Sexually Transmitted Diseases original language

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ADAPTATION OF MULTILINGUAL STACKED BOTTLE-NECK NEURAL NETWORK STRUCTURE FOR NEW LANGUAGE

ADAPTATION OF MULTILINGUAL STACKED BOTTLE-NECK NEURAL NETWOR...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Frantisek Grezl Martin Karafiat Karel Vesely Brno University of Technology Speech@FIT and IT4I Center of Excellence Brno Czech Republic

ISBN: (纸本)9781479928941

The neural network based features became an inseparable part of state-of-the-art LVCSR systems. In order to perform well, the network has to be trained on a large amount of in-domain data. With the increasing emphasis on fast development of ASR system on limited resources, there is an effort to alleviate the need of in-domain data. To evaluate the effectiveness of other resources, we have trained the Stacked Bottle-Neck neural networks structure on multilingual data investigating several training strategies while treating the target language as the unseen one. Further, the systems were adapted to the target language by re-training. Finally, we evaluated the effect of adaptation of individual NNs in the Stacked Bottle-Neck structure to find out the optimal adaptation strategy. We have shown that the adaptation can significantly improve system performance over both, the multilingual network and network trained only on target data. The experiments were performed on Babel Year 1 data.

关键词： object oriented programming languages Neural network NETWORK STRUCTURE System performance

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Design Patterns for Gas Optimization in Ethereum

Design Patterns for Gas Optimization in Ethereum

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International Workshop on Blockchain oriented Software Engineering (IWBOSE)

作者： Lodovica Marchesi Michele Marchesi Giuseppe Destefanis Giulio Barabino Danilo Tigano DMI University of Cagliari Cagliari Italy Computer Science Brunel University London United Kingdom DITEN University of Genova Genova

ISBN: (数字)9781728162782

ISBN: (纸本)9781728162799

Blockchain technology is an emerging technology that allows new forms of decentralized architectures, designed to generate trust among users, without the intervention of mediators or knowledge between the parties. Since 2015, thanks to the introduction of Smart Contracts by Ethereum, it is possible to run programs on the blockchain, greatly extending the potential of this technology. The programming of Smart Contract, through the Solidity language is different from the traditional one. First of all, any action that requires to modify the blockchain costs gas, which corresponds to a fraction of the currency used by that given blockchain, and therefore to real money. Gas optimization is a unique challenge in this context and has obvious implications. This document aims to provide a set of design patterns and tips to help gas saving in developing Smart Contracts on Ethereum. The provided patterns are presented divided into five main categories, based on their features.

关键词： contracts electronic money object-oriented languages peer-to-peer computing contract Peer to peer computing object oriented programming languages design pattern Gases Money

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INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER MATCHED AND MISMATCHED ACOUSTICAL CONDITIONS

INVESTIGATION ON CROSS- AND MULTILINGUAL MLP FEATURES UNDER ...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Zoltan Tuske Joel Pinto Daniel Willett Ralf Schluter Human Language Technology and Pattern Recognition Computer Science Department RWTH Aachen University Nuance Communications Deutschland GmbH

ISBN: (纸本)9781479903573

In this paper, Multi Layer Perceptron (MLP) based multilingual bottleneck features are investigated for acoustic modeling in three languages -- German, French, and US English. We use a modified training algorithm to handle the multilingual training scenario without having to explicitly map the phonemes to a common phoneme set. Furthermore, the cross-lingual portability of bottleneck features between the three languages are also investigated. Single pass recognition experiments on large vocabulary SMS dictation task indicate that (1) multilingual bottleneck features yield significantly lower word error rates compared to standard MFCC features (2) multilingual bottleneck features are superior to monolingual bottleneck features trained for the target language with limited training data, and (3) multilingual bottleneck features are beneficial in training acoustic models in a low resource language where only mismatched training data is available - by exploiting the more matched training data from other languages.

关键词： MLP Bottleneck Multilingual Mismatched acoustical condition Traffic Bottlenecks CSRP3 gene Multilingualism object oriented programming languages Language acoustic modeling Training data Phonemes Error analysis

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DCU-Symantec Submission for the WMT 2012 Quality Estimation Task 12

DCU-Symantec Submission for the WMT 2012 Quality Estimation ...

引用

Workshop on Statistical Machine Translation

作者： Raphael Rubino Jennifer Foster Joachim Wagner Johann Roturier Rasul Samad Zadeh Kaljahi Fred Hollowood Dublin City University Symantec Ireland

ISBN: (纸本)9781622765928;1622765923

This paper describes the features and the machine learning methods used by Dublin City University (DCU) and SYMANTEC for the WMT 2012 quality estimation task. Two sets of features are proposed: one constrained, i.e. respecting the data limitation suggested by the workshop organisers, and one unconstrained, i.e. using data or tools trained on data that was not provided by the workshop organisers. In total, more than 300 features were extracted and used to train classifiers in order to predict the translation quality of unseen data. In this paper, we focus on a subset of our feature set that we consider to be relatively novel: features based on a topic model built using the Latent Dirichlet Allocation approach, and features based on source and target language syntax extracted using part-of-speech (POS) taggers and parsers. We evaluate nine feature combinations using four classification-based and four regression-based machine learning techniques.

关键词： DCU-Symantec Submission Quality reduced mass Parser Artificial Intelligence Machine Learning Workshops workshops Classifiers object oriented programming languages dichloralurea taggers DCU Subset

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Syntax-aware Phrase-based Statistical Machine Translation: System Description 12

Syntax-aware Phrase-based Statistical Machine Translation: S...

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Workshop on Statistical Machine Translation

作者： Ulrich Germann University of Toronto Toronto Ontario Canada

ISBN: (纸本)9781622765928

We present a variant of phrase-based SMT that uses source-side parsing and a constituent reordering model based on word alignments in the word-aligned training corpus to predict hierarchical block-wise reordering of the input. Multiple possible translation orders are represented compactly in a source order lattice. This source order lattice is then annotated with phrase-level translations to form a lattice of tokens in the target language. Various feature functions are combined in a log-linear fashion to evaluate paths through that lattice.

关键词： crystal lattices object oriented programming languages machine translation system token Byways variants Translations Parsing Surface mount technology

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