自纠正词对齐

词对齐是统计机器翻译系统的重要一环,但词对齐的获得往往基于序列模型的计算,而没有考虑语言的结构化信息及语言特征,从而造成词对齐中出现一些不符合语言特征的结果。文中提出一种词对齐的自纠正机制,以纠正词对齐中的错误部分。该机制使用一些语言学上的先验知识,对词对齐结果进行由粗颗粒度到细颗粒度的纠正。首先采用基于标点的方法对句对进行粗粒度化纠正,然后采用基于统计特征的方法对子句对进行细粒度化纠正。该自纠正过程不需要借助任何其他词对齐工具和新语料。实验结果显示,自纠正词对齐显著提高了词对齐的准确率,并提高了机器翻译的质量,其中粗粒度的纠正方法对翻译质量的提高最为显著,细粒度的纠正方法也提升了翻译质量,最终通过结合粗颗粒度和细颗粒度的纠正方法,使翻译结果相对基准系统取得了显著的提高。     

基于语义相似度并运用语言学知识进行双语语句词对齐

一、引言自八十年代以来,基于统计(Statistics-Based)和基于实例(Example-Based)方法的出现及其广泛应用给机器翻译的研究工作注入了新的活力,标志着机器翻译进入了一个新时期。这两种方法共同的特点是:都需要一个双语语料库(Bilin-gual Corpora)直接或间接地作为翻译的知识库。这种双语语料库中包含了原文和译文相互对应的语言信息,是支持机器翻译的最为宝贵的资源。双语语料比单语种提供了更多的信息。在近些年里,在对篇章、段落、句子对齐进行了大量的研究之后,许多工作已经     

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。     

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.     

基于统计的锚点词候选集的获取

段对齐是在双语语料库中把各个段和它们的相应译文建立起对应关系,进而为以后的句子级对齐、短语级对齐和词汇级对齐提供资源。它在整个对齐研究中起到一个承上启下的作用。利用锚点词信息完成段对齐是一种常用且有效的方法。锚点词要求数量少准确率高,同时更为重要的是它要求具有可以说明两个段之间有对应关系的相对明显的特征,这就是说并不是文章中的每个词都可以做锚点词,锚点词也不是越多越好,这也决定了获取过程中不能借助字典等辅助信息,而必须使用其它方法。文章提出一种新的锚点词候选集的获取方法,通过统计和相似计算来得到锚点词的候选集信息。通过控制统计串的出现频数和相似度的大小就可得到令人满意的可用的结果。实验结果表明,当取高阈值且高相似度时,就可得到很高的准确率。因而该方法是一种获取锚点词的有效方法。     

基于统计和词典方法相结合的韩汉双语语料库名词短语对齐

韩汉双语语料库短语对齐对于基于实例的韩汉机器翻译系统具有重要意义,该文从韩国语名词短语结构特点出发,在基于统计和基于词典的词对齐方法进行试验分析的基础上,提出了基于词对齐位置信息的韩汉双语语料库名词短语对齐方法。该方法通过基于统计的方法获得词对齐位置信息,在此基础上利用基于词典方法的相似度计算进行词对齐校正;根据以上结果,该文通过韩国语名词短语左右边界规则抽取名词短语及其汉语译文,利用关联度度量方法进行过滤,实现名词短语对齐。实验结果表明,在较大规模语料库情况下,该方法取得了较好的短语对齐结果。     

一种基于改进隐马尔克夫模型的词语对齐方法

该文在基本隐马尔克夫模型的基础之上,利用句法知识来改进词语对齐,把英语的短语结构树距离和基本隐马尔克夫模型相结合进行词语对齐。与基本隐马尔克夫模型相比,这个模型可以降低词语对齐的错误率,并且提高统计机器翻译系统BLEU值,从而提高机器翻译质量。     

Large-scale automatic extraction of an English-Chinese translation lexicon

We report experimental results on automatic extraction of an English-Chinese translation lexicon, by statistical analysis of a large parallel corpus, using limited amounts of linguistic knowledge. To our knowledge, these are the first empirical results of the kind between an Indo-European and non-Indo-European language for any significant vocabulary and corpus size. The learned vocabulary size is about 6,500 English words, achieving translation precision in the 86–96% range, with alignment proceeding at paragraph, sentence, and word levels. Specifically, we report (1) progress on the HKUST English-Chinese Parallel Bilingual Corpus, (2) experiments supporting the usefulness of restricted lexical cues for statistical paragraph and sentence alignment, and (3) experiments that question the role of hand-derived monolingual lexicons for automatic word translation acquisition. Using a hand-derived monolingual lexicon, the learned translation lexicon averages 2.33 Chinese translations per English entry, with a manually-filtered precision of 95.1%, and an automatically-filtered weighted precision of 86.0%. We then introduce a fully automatic two-stage statistical methodology that is able to learn translations for collocations. A statistically-learned monolingual Chinese lexicon is first used to segment the Chinese text, before applying bilingual training to produce 6,429 English entries with 2.25 Chinese translations per entry. This method improves the manually-filtered precision to 96.0% and the automatically-filtered weighted precision to 91.0%, an error rate reduction of 35.7% from using a hand-derived monolingual lexicon.     

TransSearch: from a bilingual concordancer to a translation finder

As basic as bilingual concordancers may appear, they are some of the most widely used computer-assisted translation tools among professional translators. Nevertheless, they still do not benefit from recent breakthroughs in machine translation. This paper describes the improvement of the commercial bilingual concordancer TransSearch in order to embed a word alignment feature. The use of statistical word alignment methods allows the system to spot user query translations, and thus the tool is transformed into a translation search engine. We describe several translation identification and postprocessing algorithms that enhance the application. The excellent results obtained using a large translation memory consisting of 8.3 million sentence pairs are confirmed via human evaluation.     

藏文自动分词系统的设计

语料库作为基本的语言数据库和知识库,是各种自然语言处理方法实现的基础。随着统计方法在自然语言处理中的广泛应用,语料库建设已成为重要的研究课题。自动分词是句法分析的一项不可或缺的基础性工作,其性能直接影响句法分析。本文通过对85万字节藏语语料的统计分析和藏语词的分布特点、语法功能研究,介绍基于词典库的藏文自动分词系统的模型,给出了切分用词典库的结构、格分块算法和还原算法。系统的研制为藏文输入法研究、藏文电子词典建设、藏文字词频统计、搜索引擎的设计和实现、机器翻译系统的开发、网络信息安全、藏文语料库建设以及藏语语义分析研究奠定了基础。     

基于统计机器翻译的汉维词对齐研究

描述了一个基于统计机器翻译的汉维词对齐系统。系统处理过程分为两个模块:预处理和词对齐。预处理过程包括汉文文本预处理和维吾尔文文本预处理,其中维吾尔文文本预处理过程为:首先将维吾尔文转换成拉丁维文,然后将拉丁维文中个别字符替换为无歧义的字符。词对齐实现过程:首先利用IBM Model1-3,然后结合Och等人提出的启发式的思路进行优化,构建基于统计机器翻译的汉维词对齐系统。实验结果表明此系统可行。     

融合词簇约束的汉越跨语言词嵌入

针对传统跨语言词嵌入方法在汉越等差异较大的低资源语言上对齐效果不佳的问题,提出一种融合词簇对齐约束的汉越跨语言词嵌入方法。通过独立的单语语料训练获取汉越单语词嵌入,使用近义词、同类词和同主题词3种不同类型的关联关系,充分挖掘双语词典中的词簇对齐信息以融入到映射矩阵的训练过程中,使映射矩阵进一步学习到不同语言相近词间具有的一些共性特征及映射关系,根据跨语言映射将两种语言的单语词嵌入映射至同一共享空间中对齐,令具有相同含义的汉语与越南语词嵌入在空间中彼此接近,并利用余弦相似度为空间中每一个未经标注的汉语单词查找对应的越南语翻译构建汉越对齐词对,实现跨语言词嵌入。实验结果表明,与传统有监督及无监督的跨语言词嵌入方法Multi_w2v、Orthogonal、VecMap、Muse相比,该方法能有效提升映射矩阵在非标注词上的泛化性,改善汉越低资源场景下模型对齐效果较差的问题,其在汉越双语词典归纳任务P@1和P@5上的对齐准确率相比最好基线模型提升了2.2个百分点。     

基于判别式模型的蒙英词对齐方法

词对齐是自然语言处理领域的基础性研究课题之一。文中提出基于链式条件随机场(CRF)判别式模型的蒙古文-英文词对齐方法。该方法根据蒙古文和英文之间的差异,选择词形、词汇、词性等信息作为特征,建立双层CRF词对齐模型:在第1层利用CRF模型实现子块的对齐;在第2层利用CRF模型得到块内词之间的对齐结果。通过人工构建的词对齐语料开展相应实验。实验结果表明,该方法有效提高蒙英词对齐质量。     

Unsupervised statistical text simplification using pre-trained language modeling for initialization

Unsupervised text simplification has attracted much attention due to the scarcity of high-quality parallel text simplification corpora. Recent an unsupervised statistical text simplification based on phrase-based machine translation system (UnsupPBMT) achieved good performance, which initializes the phrase tables using the similar words obtained by word embedding modeling. Since word embedding modeling only considers the relevance between words, the phrase table in UnsupPBMT contains a lot of dissimilar words. In this paper, we propose an unsupervised statistical text simplification using pre-trained language modeling BERT for initialization. Specifically, we use BERT as a general linguistic knowledge base for predicting similar words. Experimental results show that our method outperforms the state-of-the-art unsupervised text simplification methods on three benchmarks, even outperforms some supervised baselines.     

汉英短语翻译对的自动抽取

介绍从平行语料库中如何抽取双语短语翻译对。首先用统计模型正则期望从汉语专利语料库中抽取汉语短语。抽取的短语利用统计知识和语言学知识来过滤,使得过滤后汉语短语的正确率较高;其次,利用词对齐工具Giza++从汉英平行语料库中抽取词汇对齐,在词汇对齐的基础上利用开源工具Moses抽取汉英短语对齐,根据短语对齐与抽取出的高质量汉语短语的交集来抽取候选的汉英互译的源语言短语;接着使用停用词、对数似然估计法LLR和上下文熵来对英语短语翻译进行过滤。实验结果表明,过滤后,抽取的汉语短语准确率为97.6%,汉英短语翻译对的准确率为92.4%。     

基于LSTM的蒙汉机器翻译的研究

由于内蒙古地区蒙汉机器翻译水平落后、平行双语语料规模较小,利用传统的统计机器翻译方法会出现数据稀疏以及训练过拟合等问题,导致翻译质量不高。针对这种情况,提出基于LSTM的蒙汉神经机器翻译方法,通过利用长短时记忆模型构建端到端的神经网络框架并对蒙汉机器翻译系统进行建模。为了更有效地理解蒙古语语义信息,根据蒙古语的特点将蒙古文单词分割成词素形式,导入模型,并在模型中引入局部注意力机制计算与目标词有关联的源语词素的权重,获得蒙古语和汉语词汇间的对齐概率,从而提升翻译质量。实验结果表明,该方法相比传统蒙汉翻译系统提高了翻译质量。     

面向统计机器翻译的重对齐方法研究

词对齐是统计机器翻译中的重要技术之一。该文提出了一种重对齐方法,它在IBM models获得的正反双向词对齐的基础上,确定出正反双向对齐不一致的部分。之后,对双向词对齐不一致的部分进行重新对齐以得到更好的对称化的词对齐结果。此外,该文提出的方法还可以利用大规模单语语料来强化对齐结果。实验结果表明,相比在统计机器翻译中广泛使用的基于启发信息的词对齐对称化方法,该文提出的方法可以使统计机器翻译系统得到更高的翻译准确率。     

基于长时间跨度语料的词义演变计算研究

该文收集了自晚清到21世纪间长达144年的连续历时报刊语料,通过统计分析和词语分布式表示两类方法展开研究,计算并辅助识别汉语词语的词义历时演变现象。采用TF-IDF、词频比例等多种统计分析的评价指标和目标词语在文段中的共现实词及其重合度挖掘出现词义演变的词语。针对历时语料上不同时间段的词向量对齐,采用SGNS训练词向量加正交矩阵投影、SGNS递增训练和“锚点词”二阶词向量表示三种方法,其中以SGNS递增训练效果最佳。针对自动发现的词义演变现象,采用目标词历时自相似度和锚点词历时相似度的分析方法,并利用近邻词来明确目标词变迁前后的词义。     

Identifying word evolution by incorporating PoS and avoiding alignment of temporal words

Identifying word evolution is important for understanding cultural and societal changes. The key to accurate identification of word evolution is to distinguish word semantics. Recently methods based on low-dimensional embedding representation for words were proposed but they require the alignment of word embeddings across different time periods. This process is computationally expensive, prohibitively time consuming and suffering from contextual variability. In this paper, we propose a method to learn low-dimensional time-aware embeddings using both statistical and POS (part-of-speech) tagging information of words. Besides, the proposed method bypasses the computationally expensive step of aligning the word embeddings by tagging each word with a time prefix and encoding them into the common vector space. The learnt temporal embeddings better reveal semantic changes over time. A comprehensive experiment is conducted on Google Books N-gram (throughout 100 years). When compared with other three top-performing temporal embedding methods (PPMI, SVD, SGNS), our method achieves state-of-the-art in terms of time complexity, precision, recall, F1-score and the number of words identified to have changed in meaning, respectively. Additionally, we provide an intuitive illustration of the semantic evolution of the interesting words identified with our method.