An efficient dictionary machine using hexagonal processor arrays

Dictionary machine is an important VLSI system performing high speed data archival operations. In this paper, we present a design which can efficiently implement dictionary machines in VLSI processor arrays. In order to effectively process the operations of dictionary machine, hexagonal mesh is selected as the host topology in which two different networks for update and query operation are embedded. The proposed design is simple to implement as well as allows high throughput     

基于词典模型融合的神经机器翻译

无监督神经机器翻译仅利用大量单语数据,无需平行数据就可以训练模型,但是很难在2种语系遥远的语言间建立联系。针对此问题,提出一种新的不使用平行句对的神经机器翻译训练方法,使用一个双语词典对单语数据进行替换,在2种语言之间建立联系,同时使用词嵌入融合初始化和双编码器融合训练2种方法强化2种语言在同一语义空间的对齐效果,以提高机器翻译系统的性能。实验表明,所提方法在中-英与英-中实验中比基线无监督翻译系统的BLEU值分别提高2.39和1.29,在英-俄和英-阿等单语实验中机器翻译效果也显著提高了。     

双语词典在统计机器翻译中的应用

在当前的基于统计的翻译方法中,双语语料库的规模、词对齐的准确率对于翻译系统的性能有很大的影响。虽然大规模语料库可以改善词语对齐的准确度,提高系统的性能,但同时会以增加系统的负载为代价,因此目前对于统计机器翻译方法的研究在使用大规模语料库的基础上,同时寻求其他可以提高系统性能的方法。针对以上问题,提出一种把双语词典应用在统计机器翻译中的方法,不仅优化了词对齐的准确率,而且得出质量更高的翻译结果,在一定程度上缓解了数据稀疏问题。     

机器辅助翻译中模糊查词典和快速录入单词

本文介绍在单词记忆不准确的情况下, 如何查找词典以及如何只键入单词中的几个字母快速录入单词的算法。在辅助翻译和写作系统中, 词汇级的帮助是最基本的, 主要指词典查询。但很多情况下, 用户单词记忆不很准确, 只记住了几个字母, 本文解决这种情况下的模糊查询问题。这种模糊技术的核心是全文检索, 依赖于词典的特殊索引。在解决了模糊查询之后, 利用全文检索技术以及模糊二分查找技术进一步开发了写作系统中的快速录入功能。     

基于短语统计翻译的汉维机器翻译系统

描述了一种基于短语统计翻译的汉维机器翻译系统。首先使用汉维语料进行训练,得到语言模型和翻译模型;再利用训练好的模型对源语句进行解码,以得到最佳的翻译语句。解码的核心算法是柱搜索（beam search）算法。其中维文语料使用的是拉丁维文。实验结果表明,基于短语的统计机器翻译方法可以快速有效地构建一个汉维机器翻译平台。     

Batch-mode semi-supervised active learning for statistical machine translation

The development of high-performance statistical machine translation (SMT) systems is contingent on the availability of substantial, in-domain parallel training corpora. The latter, however, are expensive to produce due to the labor-intensive nature of manual translation. We propose to alleviate this problem with a novel, semi-supervised, batch-mode active learning strategy that attempts to maximize in-domain coverage by selecting sentences, which represent a balance between domain match, translation difficulty, and batch diversity. Simulation experiments on an English-to-Pashto translation task show that the proposed strategy not only outperforms the random selection baseline, but also traditional active selection techniques based on dissimilarity to existing training data.     

VENUS: Two-phase machine translation system

NEC has been developing a Japanese-English bi-directional machine translation system called VENUS (Vehicle for Natural language Understanding & Synthesis) in order to reduce the increasing cost of the manual translation of vast amounts of in-house technical documents. In addition, a translation support subsystem has been developed on the basis of VENUS, and extended to have the requisite facilities to prepare translated documents, such as document entry, editing, translation, printing, management, etc.This paper briefly introduces the current status of the VENUS translation system, and the basic idea for the system development.     

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.     

英汉机器翻译系统中的词性标注研究

词性标注是英汉机器翻译系统中一个基础性的研究课题。提出了一种基于决策树的词性标注的非监督学习算法,在只有一个词库的有限条件下,通过决策树进行词性标注的非监督学习,生成词性标注规则。     

A novel method for dictionary translation

The paper addresses the problem of automatic dictionary translation.The proposed method translates a dictionary by means of mining repositories in the source and target languages, without any directly given relationships connecting the two languages. It consists of two stages: (1) translation by lexical similarity, where words are compared graphically, and (2) translation by semantic similarity, where contexts are compared. In the experiments Polish and English version of Wikipedia were used as text corpora. The method and its phases are thoroughly analyzed. The results allow implementing this method in human-in-the-middle systems.     

Relational-grammar-based generation in the JETS Japanese-English machine translation system

This paper describes the design and function of the English generation phase in JETS, a minimal transfer, Japanese-English machine translation system that is based on the linguistic framework of relational grammar. To facilitate the development of relational grammar generators, we have built a generator shell that provides a high-level relational grammar rule-writing language and is independent of both the natural language and the application. The implemented English generator (called GENIE) maps abstract canonical structures, representing the basic predicate-argument structures of sentences, into well-formed English sentences via a two-stage plan-and-execute design. The modularity inherent in the plan-and-execute design permits the development of a very general and stable deterministic execution grammar. Another major feature of the GENIE generator is that it iscategory-driven, i.e., planning rules and execution rules are distributed over a part-of-speech hierarchy (down to individual lexical items) and are invoked via an inheritance mechanism only if appropriate for the category being processed. Categorydriven processing facilitates the handling of exceptions. The use of a syntactic planner and category-driven processing together provide a great deal of flexibility without sacrificing determinism in the generation process.     

Providing machine tractable dictionary tools

Machine readable dictionaries (Mrds) contain knowledge about language and the world essential for tasks in natural language processing (Nlp). However, this knowledge, collected and recorded by lexicographers for human readers, is not presented in a manner for Mrds to be used directly for Nlp tasks. What is badly needed are machine tractable dictionaries (Mtds): Mrds transformed into a format usable for Nlp. This paper discusses three different but related large-scale computational methods to transform Mrds into Mtds. The Mrd used is The Longman Dictionary of Contemporary English (Ldoce). The three methods differ in the amount of knowledge they start with and the kinds of knowledge they provide. All require some handcoding of initial information but are largely automatic. Method I, a statistical approach, uses the least handcoding. It generates relatedness networks for words in Ldoce and presents a method for doing partial word sense disambiguation. Method II employs the most handcoding because it develops and builds lexical entries for a very carefully controlled defining vocabulary of 2,000 word senses (1,000 words). The payoff is that the method will provide an Mtd containing highly structured semantic information. Method III requires the handcoding of a grammar and the semantic patterns used by its parser, but not the handcoding of any lexical material. This is because the method builds up lexical material from sources wholly within Ldoce. The information extracted is a set of sources of information, individually weak, but which can be combined to give a strong and determinate linguistic data base.     

Improving statistical machine translation using shallow linguistic knowledge

We describe methods for improving the performance of statistical machine translation (SMT) between four linguistically different languages, i.e., Chinese, English, Japanese, and Korean by using morphosyntactic knowledge. For the purpose of reducing the translation ambiguities and generating grammatically correct and fluent translation output, we address the use of shallow linguistic knowledge, that is: (1) enriching a word with its morphosyntactic features, (2) obtaining shallow linguistically-motivated phrase pairs, (3) iteratively refining word alignment using filtered phrase pairs, and (4) building a language model from morphosyntactically enriched words. Previous studies reported that the introduction of syntactic features into SMT models resulted in only a slight improvement in performance in spite of the heavy computational expense, however, this study demonstrates the effectiveness of morphosyntactic features, when reliable, discriminative features are used. Our experimental results show that word representations that incorporate morphosyntactic features significantly improve the performance of the translation model and language model. Moreover, we show that refining the word alignment using fine-grained phrase pairs is effective in improving system performance.     

跨平台系统虚拟机的二进制翻译优化

在跨平台系统虚拟机原型ARCH-BRIDGE的基础上,提出了一种基本块级的动态二进制翻译优化方法,通过两阶段翻译、基于虚拟寄存器的优化翻译及延迟机器状态提交等技术,可在不采用中间表示及确保精确异常的情况下,有效去除二进制翻译所引入的冗余。测试表明,优化后的ARCH-BRIDGE在翻译开销明显优于QEMU的同时,翻译块尺寸和翻译冗余得到了大幅降低,并且SPEC CPU2006、NBENCH及OS引导的性能均得到了显著提升。     

A generalized simultaneous access dictionary machine

A simultaneous access design of a dictionary machine which supports insert, delete, and search operations is presented. The design is able to handle p accesses simultaneously and allows redundant accesses to occur. In the design, processors performing insert or delete operations are free to perform other tasks after submitting their accesses to the design; processors that perform search operations get their response in O(log N) time. Compared to all sequential access designs of a dictionary which require O(p ) time to process p accesses, the presented design provides much higher throughput; specifically, O(p/log p) times better. It also provides a fast mechanism to avoid the sequential access bottleneck in any large multiprocessor system     

基于短语的阿拉伯语到中文的机器翻译系统*

使用基于短语的统计翻译方法,搭建了一个简易的阿拉伯语到中文的翻译系统。核心的解码器采用了loglinear直接翻译模型进行开发,在系统中使用了大量的开源软件进行语料库的预处理,并讨论了该方向上尚未解决的问题和未来的发展趋势。     

Substring-based machine translation

Machine translation is traditionally formulated as the transduction of strings of words from the source to the target language. As a result, additional lexical processing steps such as morphological analysis, transliteration, and tokenization are required to process the internal structure of words to help cope with data-sparsity issues that occur when simply dividing words according to white spaces. In this paper, we take a different approach: not dividing lexical processing and translation into two steps, but simply viewing translation as a single transduction between character strings in the source and target languages. In particular, we demonstrate that the key to achieving accuracies on a par with word-based translation in the character-based framework is the use of a many-to-many alignment strategy that can accurately capture correspondences between arbitrary substrings. We build on the alignment method proposed in Neubig et al. (Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. Portland, Oregon, pp. 632–641, 2011), improving its efficiency and accuracy with a focus on character-based translation. Using a many-to-many aligner imbued with these improvements, we demonstrate that the traditional framework of phrase-based machine translation sees large gains in accuracy over character-based translation with more naive alignment methods, and achieves comparable results to word-based translation for two distant language pairs.