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Analyzing Chinese English by the functional equivalence theory

Analyzing Chinese English by the functional equivalence theo...

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2016 4th International Conference on Management Science,Education Technology,Arts,Social Science and Economics(MSETASSE 2016)

作者： Zhao Hui Min Bohai university

ISBN: (纸本)9781510834071

Over the past few years, with China's rising international status, the international arena has a more right to speak. The emergence of a large number of words with Chinese characteristics has played an important role in China's propaganda. When the eastern and Western cultures are blended, it is the most important task for the translators to spread the Chinese and Western culture to the western countries. The essence of translation is to translate one language into another, which is communicated to the audience. Translation theory is often divided into translation and interpretation, translation and translation skills such as transliteration, literal translation and free translation techniques, analysis of the basic skills of interpreting translation skills for the purpose of this paper is to reference the translation. The famous American translation theorist Nida "s Functional Equivalence Based on the theory, he believes that between the source language and the target language can be achieved at four levels of equivalence: vocabulary equivalence, syntactic equivalence, stylistic equivalence and textual equivalence. This paper analyzes the application of translation methods such as transliteration, literal translation and free translation from four levels of equivalence, and explores the translation of Chinese characteristics into English This paper discusses the English translation of Chinese tourism texts from the perspective of functional equivalence.

关键词： Functional equivalence theory Chinese English Chinese characteristics English translation skills Translation strategies Translations Translation functionally equivalent Translation Process equivalence original language European cultures object oriented programming languages Chinese Chinese philosophy Chinese art English English language translator basic skill

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Stochastic Parse Tree Selection for an Existing RBMT System 11

Stochastic Parse Tree Selection for an Existing RBMT System

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Workshop on statistical machine translation

作者： Christian Federmann Sabine Hunsicker DFKI GmbH Language Technology Lab Saarbrücken Germany

ISBN: (纸本)9781937284121

In this paper we describe our hybrid machine translation system with which we participated in the WMT11 shared translation task for the English→German language pair. Our system was able to outperform its RBMT baseline and turned out to be the best-scored participating system in the manual evaluation. To achieve this, we extended an existing, rule-based MT system with a module for stochastic selection of analysis parse trees that allowed to better cope with parsing errors during the system's analysis phase. Due to the integration into the analysis phase of the RBMT engine, we are able to preserve the benefits of a rule-based translation system such as proper generation of target language text. Additionally, we used a statistical tool for terminology extraction to improve the lexicon of the RBMT system. We report results from both automated metrics and human evaluation efforts, including examples which show how the proposed approach can improve machine translation quality.

关键词： terminology extraction parsing tree Dictionaries as Topic Parsing machine translation reduced mass Stochastic machine translation system Translation systems object oriented programming languages DNA Integration integration

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Automated Translation of Semantic Relationships 10

Automated Translation of Semantic Relationships

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International conference on computational linguistics

作者： Dmitry Davidov Ari Rappoport Hebrew University of Jerusalem

ISBN: (纸本)9787302234562

We present a method for translating semantic relationships between languages where relationships are defined as pattern clusters. Given a pattern set which represents a semantic relationship, we use the web to extract sample term pairs of this relationship. We automatically translate the obtained term pairs using multilingual dictionaries and disambiguate the translated pairs using web counts. Finally we discover the set of most relevant target language patterns for the given relationship. The obtained pattern set can be utilized for extraction of new relationship examples for the target language. We evaluate our method on 11 diverse target languages. To assess the quality of the discovered relationships, we use an automatically generated cross-lingual SAT analogy test, WordNet relationships, and concept-specific relationships, achieving high precision. The proposed framework allows fully automated cross-lingual relationship mining and construction of multilingual pattern dictionaries without relying on parallel corpora.

关键词： object oriented programming languages Web search engines Automatic translation Semantics Language Dictionaries as Topic AUTOMATIC solidification

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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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A Logical Theory of Belief Dynamics for Resource-Bounded Agents 16

A Logical Theory of Belief Dynamics for Resource-Bounded Age...

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International Conference on Autonomous Agents and Multiagent Systems

作者： Philippe Balbiani David Fernandez-Duque Emiliano Lorini IRIT-CNRS Toulouse University IRIT Toulouse University

ISBN: (纸本)9781510855083

The paper presents a new logic for reasoning about the formation of beliefs through perception or through inference in non-omniscient resource-bounded agents. The logic distinguishes the concept of explicit belief from the concept of background knowledge. This distinction is reflected in its formal semantics and axiornatics: (i) we use a non-standard semantics putting together a neighbourhood semantics for explicit beliefs and relational semantics for background knowledge, and (ii) we have specific axioms in the logic high-lighting the relationship between the two concepts Mental operations of perceptive type and inferential type, having effects on epistemic states of agents, are primitives in the object language of the logic. At the semantic level, they are modelled as special kinds of model-update operations, in the style of dynamic epistemic logic (DEL). Results about axiomatization, decidability and complexity for the logic are given in the paper.

关键词： Theory Epistemic logic Cognitive agents Resource-bounded reasoning Formal logic Beliefs Semantics object oriented programming languages semiotics Agents theories non-standard

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CROSS-LINGUAL AND MULTILINGUAL SPEECH EMOTION RECOGNITION ON ENGLISH AND FRENCH

CROSS-LINGUAL AND MULTILINGUAL SPEECH EMOTION RECOGNITION ON...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Michael Neumann Ngoc Thang Vu University of Stuttgart Germany

ISBN: (纸本)9781538646595

Research on multilingual speech emotion recognition faces the problem that most available speech corpora differ from each other in important ways, such as annotation methods or interaction scenarios. These inconsistencies complicate building a multilingual system. We present results for cross-lingual and multilingual emotion recognition on English and French speech data with similar characteristics in terms of interaction (human-human conversations). Further, we explore the possibility of fine-tuning a pre-trained cross-lingual model with only a small number of samples from the target language, which is of great interest for low-resource languages. To gain more insights in what is learned by the deployed convolutional neural network, we perform an analysis on the attention mechanism inside the network.

关键词： Speech Emotion Recognition Multilingual Cross-lingual CNN Attention emotion recognition Speech object oriented programming languages Multilingualism

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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...

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

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

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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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SPEAKER INVARIANT FEATURE EXTRACTION FOR ZERO-RESOURCE languages WITH ADVERSARIAL LEARNING

SPEAKER INVARIANT FEATURE EXTRACTION FOR ZERO-RESOURCE LANGU...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Taira Tsuchiya Naohiro Tawara Testuji Ogawa Tetsunori Kobayashi Department of Communications and Computer Engineering Waseda University Tokyo Japan

ISBN: (纸本)9781538646595

We introduce a novel type of representation learning to obtain a speaker invariant feature for zero-resource languages. Speaker adaptation is an important technique to build a robust acoustic model. For a zero-resource language, however, conventional model-dependent speaker adaptation methods such as constrained maximum likelihood linear regression are insufficient because the acoustic model of the target language is not accessible. Therefore, we introduce a model-independent feature extraction based on a neural network. Specifically, we introduce a multi-task learning to a bottleneck feature-based approach to make bottleneck feature invariant to a change of speakers. The proposed network simultaneously tackles two tasks: phoneme and speaker classifications. This network trains a feature extractor in an adversarial manner to allow it to map input data into a discriminative representation to predict phonemes, whereas it is difficult to predict speakers. We conduct phone discriminant experiments in Zero Resource Speech Challenge 2017. Experimental results showed that our multi-task network yielded more discriminative features eliminating the variety in speakers.

关键词： zero resource speech challenge speaker invariant feature adversarial multi-task learning fMLLR representation learning Loudspeakers object oriented programming languages Feature extraction Acoustic models Language speaker adaptation Phonemes Maximum likelihood linear regression Learning

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

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

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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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