Glossary-Based MT Engines in a Multilingual Analyst's Workstation Architecture

Fast access to information in different languages is still a major problem for many organizations. We have built a multilingual analyst's workstation integrated in the Tipster document management toolkit. The analyst workstation offers to an English-speaking analyst a variety of tools to browse sets of documents in Arabic, Japanese, Spanish and Russian, including a Unicode-based multilingual editor, and a simple machine translation functionality.The Temple project has developed an open multilingual architecture and software support for rapid development of extensible machine translation functionalities. The targeted languages are those for which natural language processing and human resources are scarce or difficult to obtain. The goal is to support rapid development of machine translation functionalities in a very short time with limited resources.Glossary-based machine-translation (GBMT) is used to provide an English gloss of a foreign document. A GBMT system uses a bilingual phrasal dictionary (glossary) to produce a phrase-by-phrase translation. Translation (based on phrase pattern-matching) is fast and accurate regarding the content of the document and browsed documents can be translated almost in real-time. A GBMT system for a language pair is also extremely simple, cheap and fast to develop. Moreover, all language resources used by the system are entirely under the control of the user.     

Simultaneous translation of lectures and speeches

With increasing globalization, communication across language and cultural boundaries is becoming an essential requirement of doing business, delivering education, and providing public services. Due to the considerable cost of human translation services, only a small fraction of text documents and an even smaller percentage of spoken encounters, such as international meetings and conferences, are translated, with most resorting to the use of a common language (e.g. English) or not taking place at all. Technology may provide a potentially revolutionary way out if real-time, domain-independent, simultaneous speech translation can be realized. In this paper, we present a simultaneous speech translation system based on statistical recognition and translation technology. We discuss the technology, various system improvements and propose mechanisms for user-friendly delivery of the result. Over extensive component and end-to-end system evaluations and comparisons with human translation performance, we conclude that machines can already deliver comprehensible simultaneous translation output. Moreover, while machine performance is affected by recognition errors (and thus can be improved), human performance is limited by the cognitive challenge of performing the task in real time.     

Fujitsu machine translation system: ATLAS

Due to the rapid advancement of both computer technology and linguistic theory, machine translation systems are now coming into practical use.Fujitsu has two machine translation systems, ATLAS-I is a syntax-based machine translation system which translates English into Japanese. ATLAS II is a semantic-based system which aims at high quality multilingual translation. In this paper, both the ATLAS-I and ATLAS II translation mechanisms are explained.     

基于Hadoop与XMPP的云翻译系统的研究与实现

针对传统机器翻译系统准确性差、人工翻译成本高等缺陷,提出了一种基于Hadoop云计算框架与XMPP协议的云翻译系统解决方案,结合传统翻译技术和Hadoop云计算框架,利用XMPP在异构系统之间的互通,建立用户、译员和对象的三方互助云平台.该系统可挖掘互助沟通过程中的庞杂的语料资源,具有语料库数据量大,翻译准确、翻译效率高、智能性强等特点,解决了人工翻译成本高、机器翻译歧义性大等问题,实现了不同语种人群通过互联网进行文字即时通信时的多语无障碍沟通.     

Web Service构架下的多语种构件库系统及实现

论文以构件库技术和Web Services技术为基础,提出了将两种技术结合的Web services构件库的基本框架,结合新疆多语种语言的特点及应用需求,设计开发了多语种语言构件库管理系统.验证了Web Service技术开发模式具有良好的互操作性、平台无关性和可伸缩性.多语种构件库管理系统的建设填补了多语种语言民族地区相关软件业领域建设的空白,其开发模式及相关技术解决方案对促进面向中亚西亚出口软件开发提供重要技术支持.     

Translation Agent: A New Metaphor for Machine Translation

We present agent metaphor as a novel interactive system to promote the efficiency in machine translation mediated communication. Machine translation is increasingly used to support multilingual communication. In the traditional, transparent-channel way of using machine translation for the multilingual communication, translation errors are ignorable, due to the quality limitation of current machine translators. Those translation errors will break the communication and lead to miscommunication. We propose to shift the paradigm from the transparent-channel metaphor to the human-interpreter metaphor, which motives the interactions between the users and the machine translator. Following this paradigm shifting, the interpreter (agent) encourages the dialog participants to collaborate, as their interactivity will be helpful in reducing the number of translation errors, the noise of the channel. We examine the translation issues raised by multilingual communication, and analyze the impact of interactivity on the elimination of translation errors. We propose an implementation of the agent metaphor, which promotes interactivity between dialog participants and the machine translator. We design the architecture of our agent, analyze the interaction process, describe decision support and autonomous behavior, and provide an example of preparing repair strategy. We conduct an English-Chinese communication task experiment on tangram arrangement. The experiment shows that compared to the transparent-channel metaphor, our agent metaphor reduces human communication effort by 21.6%.     

汉英机器翻译的时态处理问题解析

人类社会步人信息时代．跨地区交流与合作不断加强,语言不通成为沟通的一大障碍,机器翻译研究工作具有广泛的社会效益和经济效益。伴随我国综合国力的增强,汉语在国际社会中的使用越来越频繁,而英语是使用最广泛的国际性语言．汉英机器翻译研究工作显得尤为重要。在汉英机器翻译过程中必须处理汉英时态转换问题,否则将影响翻译的准确率和译文阅读流畅度。     

神经机器翻译前沿综述

机器翻译是指通过计算机将源语言句子翻译到与之语义等价的目标语言句子的过程,是自然语言处理领域的一个重要研究方向。神经机器翻译仅需使用神经网络就能实现从源语言到目标语言的端到端翻译,目前已成为机器翻译研究的主流方向。该文选取了近期神经机器翻译的几个主要研究领域,包括同声传译、多模态机器翻译、非自回归模型、篇章翻译、领域自适应、多语言翻译和模型训练,并对这些领域的前沿研究进展做简要介绍。     

The ATR multilingual speech-to-speech translation system

In this paper, we describe the ATR multilingual speech-to-speech translation (S2ST) system, which is mainly focused on translation between English and Asian languages (Japanese and Chinese). There are three main modules of our S2ST system: large-vocabulary continuous speech recognition, machine text-to-text (T2T) translation, and text-to-speech synthesis. All of them are multilingual and are designed using state-of-the-art technologies developed at ATR. A corpus-based statistical machine learning framework forms the basis of our system design. We use a parallel multilingual database consisting of over 600 000 sentences that cover a broad range of travel-related conversations. Recent evaluation of the overall system showed that speech-to-speech translation quality is high, being at the level of a person having a Test of English for International Communication (TOEIC) score of 750 out of the perfect score of 990.     

English-Thai Translation: Initial Experiments with a Multiphase Translation System

A multiphase machine translation approach, Generate and Repair Machine Translation (GRMT), is proposed. GRMT is designed to generate accurate translations that focus primarily on retaining the linguistic meaning of the source language sentence. GRMT presently incorporates a limited multilingual translation capability. The central idea behind the GRMT approach is to generate a translationcandidate (TC) by quick and dirty machine translation (QDMT), then investigate the accuracy of that TC by translation candidate evaluation (TCE), and, if necessary, revise the translation in the repair and iterate (RI) phase. To demonstrate the GRMT approach, a translation system that translates from English to Thai has been developed. This paper presents the design characteristics and some experimental results of QDMT and also the initial design, some experiments, and proposed ideas behind TCE and RI.     

Using a language independent domain model for multilingual information extraction

The volume of electronic text in different languages, particularly on the World Wide Web, is growing significantly, and the problem of users who are restricted in the number of languages they read obtaining information from this text is becoming more widespread. This article investigates some of the issues involved in achieving multilingual information extraction (IE), describes the approach adopted in the M-LaSIE-II IE system, which addresses these problems, and presents the results of evaluating the approach against a small parallel corpus of English/French newswire texts. The approach is based on the assumption that it is possible to construct a language independent representation of concepts relevant to the domain, at least for the small well-defined domains typical of IE tasks, allowing multilingual IE to be successfully carried out without requiring full machine translation.     

英中可比语料库中多词表达自动提取与对齐

多词表达（MWE）不仅用来提高当前机器翻译系统质量,而且也用于跨语言检索和数据挖掘等其他自然语言处理领域。为此,提出了基于语义模板与基于统计工具相结合的方法从三元组可比语料库中自动提取本族英语MWE。采用基于词表和分布方法计算词语间的相似度,扩大MWE覆盖范围。利用GIZA++对齐算法提取对译的中文MWE,依据统计方法计算互译概率信息,根据概率大小,选择最佳英汉MWE互译对。实验结果表明上述方法可以有效提高MWE提取和对齐的准确率。     

Generating Arabic text in multilingual speech-to-speech machine translation framework

The interlingual approach to machine translation (MT) is used successfully in multilingual translation. It aims to achieve the translation task in two independent steps. First, meanings of the source-language sentences are represented in an intermediate language-independent (Interlingua) representation. Then, sentences of the target language are generated from those meaning representations. Arabic natural language processing in general is still underdeveloped and Arabic natural language generation (NLG) is even less developed. In particular, Arabic NLG from Interlinguas was only investigated using template-based approaches. Moreover, tools used for other languages are not easily adaptable to Arabic due to the language complexity at both the morphological and syntactic levels. In this paper, we describe a rule-based generation approach for task-oriented Interlingua-based spoken dialogue that transforms a relatively shallow semantic interlingual representation, called interchange format (IF), into Arabic text that corresponds to the intentions underlying the speaker’s utterances. This approach addresses the handling of the problems of Arabic syntactic structure determination, and Arabic morphological and syntactic generation within the Interlingual MT approach. The generation approach is developed primarily within the framework of the NESPOLE! (NEgotiating through SPOken Language in E-commerce) multilingual speech-to-speech MT project. The IF-to-Arabic generator is implemented in SICStus Prolog. We conducted evaluation experiments using the input and output from the English analyzer that was developed by the NESPOLE! team at Carnegie Mellon University. The results of these experiments were promising and confirmed the ability of the rule-based approach in generating Arabic translation from the Interlingua taken from the travel and tourism domain.     

Web-based intelligent helpdesk-support environment

With the advent of Internet technology, it is now feasible to provide effective and efficient helpdesk service over the global Internet to meet customers' requirements and satisfaction. In this research, we have designed and developed a Web-based intelligent helpdesk-support environment, WebHotLine, to support the customer service centre of a large multinational corporation in the electronics industry. The paper describes the basic architecture of the environment that supports the major functions of Web-based fault information retrieval, online multilingual translation capability, different operating modes of video-conferencing for enhanced support and direct intelligent fault diagnosis by customers or customer support engineers. As a result, WebHotLine helps to save cost in eliminating the expensive overseas telephone charges, reduction in machine down time and number of on-site visits by service engineers as in traditional helpdesk environment.     

Portable real-time software for 8-bit microprocessors

This paper discusses a method for developing efficient and portable software for 8-bit microprocessors used in real-time applications. The technique used is to design an ‘intermediate level language’ (ILL) which defines low-level primitives to support the real-time application programming and the constructs of high level languages. Thus, the high level language (HLL) program goes through two stages of translation; first to the ILL code and then to the machine code of a microprocessor. The ILL instruction set developed bridges the gap between high level languages and the poor instruction set of microprocessors. This allows the development of optimized and portable code for the microprocessors. The ILL operations, data types, data organization, control structures, synchronization, communication and multi-tasking facilities are described. The effectiveness of this technique is shown by comparing the code generated by the ILL approach with the code available for a sample real-time application written directly in assembly level language.     

基于语义选择与信息特征的英语机器翻译系统

本文基于语义选择与信息特征设计了英语自动化机器翻译系统。通过语义信息特征制定了机器翻译流程,以GIZA++为载体进行翻译,利用伯克利对准器对齐词语,基于反向转换语法,详细阐述汉语语言模式与英语翻译语言模式的结构关联特性,以语句动静配置,实现自动化机器翻译。最后通过系统测试,结果表明,与传统机器翻译系统相比,准确率显著提高,这就表明基于语义选择与信息特征的英语自动化机器翻译系统的翻译准确率较高,可为英汉机器翻译奠定坚实的基础支持。     

Language Adaptation for Entity Relation Classification via Adversarial Neural Networks

Entity relation classification aims to classify the semantic relationship between two marked entities in a given sentence,and plays a vital role in various natural language processing applications.However,existing studies focus on exploiting mono-lingual data in English,due to the lack of labeled data in other languages.How to effectively benefit from a richly-labeled language to help a poorly-labeled language is still an open problem.In this paper,we come up with a language adaptation framework for cross-lingual entity relation classification.The basic idea is to employ adversarial neural networks(AdvNN)to transfer feature representations from one language to another.Especially,such a language adaptation framework enables feature imitation via the competition between a sentence encoder and a rival language discriminator to generate effective representations.To verify the effectiveness of AdvNN,we introduce two kinds of adversarial structures,dual-channel AdvNN and single-channel AdvNN.Experimental results on the ACE 2005 multilingual training corpus show that our single-channel AdvNN achieves the best performance on both unsupervised and semi-supervised scenarios,yield-ing an improvement of 6.61％and 2.98％over the state-of-the-art,respectively.Compared with baselines which directly adopt a machine translation module,we find that both dual-channel and single-channel AdvNN significantly improve the performances(F1)of cross-lingual entity relation classification.Moreover,extensive analysis and discussion demonstrate the appropriateness and effectiveness of different parameter settings in our language adaptation framework.     

Implementation of Embedded Technology-Based English Speech Identification and Translation System

Due to the increase in globalization, communication between different countries has become more and more frequent. Language barriers are the most important issues in communication. Machine translation is limited to texts, and cannot be an adequate substitute for oral communication. In this study, a speech recognition and translation system based on embedded technology was developed for the purpose of English speech recognition and translation. The system adopted the Hidden Markov Model (HMM) and Windows CE operating system. Experiments involving English speech recognition and EnglishChinese translation found that the accuracy of the system in identifying English speech was about 88%, and the accuracy rate of the system in translating English to Chinese was over 85%. The embedded technology-based English speech recognition and translation system demonstrated a level of high accuracy in speech identification and translation, demonstrating its value as a practical application. Therefore, it merits further research and development.