Statistical Machine Translation Methods Research Articles

Chinese Natural Language Processing: From Text Categorization to Machine Translation

Abstract The level and volume of automatic computerized processing of linguistic information has become one of the most important criteria for measuring whether a country has entered the information society. The study begins with statistical linguistics and aims to process complicated Chinese information. In this paper, after establishing the word database of the Chinese language, the language model is smoothed and compressed, the Chinese character information and Chinese language information are extracted, and the processing of Chinese grammar and Chinese semantic information is emphasized. Among them, Chinese grammar processing includes Chinese word analysis and basic phrase analysis based on the maximum entropy model, and Chinese semantic processing includes Bayesian-based word sense disambiguation, semantic role labeling based on the conditional random field model, and thesaurus-based semantic similarity calculation method. In addition, SECTILE-based Chinese text categorization and statistical linguistics-based machine translation methods are explored and tested for their effectiveness in Chinese natural language processing. The results show that the total average check accuracy and check the completeness of Chinese text are 78.65% and 72.24%, respectively, and the BLEU values of the translation methods are improved by [1.62,3.73] and [0.93,5.01] compared with the Baseline method, which is able to process the Chinese information accurately. The research plays an important role in the process of information processing based on Chinese language processing.

Research on high-performance English translation based on topic model

Retelling extraction is an important branch of Natural Language Processing (NLP), and high-quality retelling resources are very helpful to improve the performance of machine translation. However, traditional methods based on the bilingual parallel corpus often ignore the document background in the process of retelling acquisition and application. In order to solve this problem, we introduce topic model information into the translation mode and propose a topic-based statistical machine translation method to improve the translation performance. In this method, Probabilistic Latent Semantic Analysis (PLSA) is used to obtains the co-occurrence relationship between words and documents by the hybrid matrix decomposition. Then we design a decoder to simplify the decoding process. Experiments show that the proposed method can effectively improve the accuracy of translation.

Design and Testing of Automatic Machine Translation System Based on Chinese-English Phrase Translation

With the development of linguistics and the improvement of computer performance, the effect of machine translation is getting better and better, and it is widely used. The automatic expression translation method based on the Chinese-English machine takes short sentences as the basic translation unit and makes full use of the order of short sentences. Compared with word-based statistical machine translation methods, the effect is greatly improved. The performance of machine translation is constantly improving. This article aims to study the design of phrase-based automatic machine translation systems by introducing machine translation methods and Chinese-English phrase translation, explore the design and testing of machine automatic translation systems based on the combination of Chinese-English phrase translation, and explain the role of machine automatic translation in promoting the development of translation. In this article, through the combination of machine translation experiments and machine automatic translation system design methods, the design and testing of machine automatic translation systems based on Chinese-English phrase translation combinations are studied to cultivate people's understanding of language, knowledge, and intelligence and then help solve other problems. Language processing issues promote the development of corpus linguistics. The experimental results in this article show that when the Chinese-English phrase translation probability table is changed from 82% to 51%, the BLEU translation evaluation system for the combination of Chinese-English phrases is improved. Automatic machine translation saves time and energy of translation work, which shows that machine translation shows its advantages due to its short development cycle and easy processing of large-scale corpora.

Efek Peningkatan Jumlah Paralel Korpus Pada Penerjemahan Kalimat Bahasa Indonesia ke Bahasa Lampung Dialek Api

Experimental observations of the effect of the number of parallel corpus on Indonesian translation into the Lampung dialect api were carried out using the statistical machine translation (SMT) method. SMT utilizes a parallel Indonesian corpus and its translation in the Lampung dialect api as a material for training data. The research strategy was carried out in three ways, namely first strategy with a corpus parallel number of 1000 sentences, the second strategy with a corpus parallel number of 2000 and the third strategy with a corpus parallel number of 3000 sentences. The research starts from the preprocessing phase followed by the training phase, namely the parallel corpus processing phase to obtain a language model and translation model. Then the testing phase, and ends with the evaluation phase. SMT testing uses 25 single sentences without out-of-vocabulary (OOV), 25 single sentences with OOV, 25 compound sentences without OOV and 25 compound sentences with OOV. The test results of translating Indonesian sentences into Lampung dialectic api are shown through the accuracy value of Bilingual Evaluation Undestudy (BLEU) obtained in testing 25 single sentences without out-of-vocabulary (OOV) in the first strategy, the second and the third are 21.49%, 59.58% and 73.21%. In testing 25 single sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 23.22%, 44.33% and 68.72%. In testing 25 compound sentences without out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 18.22%, 39.4% and 69.18%. In testing 25 compound sentences with out-of-vocabulary (OOV) obtained in the first strategy, the second and the third are 25.94%, 28.22% and 71.94%.

Tibetan-Chinese Neural Machine Translation Combining Attention Mechanism

Neural Machine Translation (NMT) has developed rapidly in recent years, and translation effects in most languages have surpassed statistical machine translation methods. The Seq2Seq framework has brought great advantages to machine translation, but the model still has great limitations in the ability to capture long-distance information. Recurrent neural networks (RNN) and LSTM networks are proposed to improve this problem, but the effect is not obvious. However, the proposal and application of the attention mechanism effectively compensated for this defect. This paper uses the attention mechanism as the basis to construct the encoder-decoder framework. This method analyzes the mechanism and principle of the attention model to implement the Tibetan-Chinese neural machine translation system. Compared with the previous neural network translation models, the results show that the translation model that incorporates the attention mechanism can obtain better translation results.

Supervised OCR Error Detection and Correction Using Statistical and Neural Machine Translation Methods

Supervised OCR Error Detection and Correction Using Statistical and Neural Machine Translation Methods

Minimum Bayes-Risk Phrase Table Pruning for Pivot-Based Machine Translation in Internet of Things

Machine translation, which will be used widely in human-computer interaction services to Internet of Things (IoT), is a key technology in artificial intelligence field. This paper presents a minimum Bayes-risk (MBR) phrase table pruning method for pivot-based statistical machine translation (SMT). The SMT system requires a great amount of bilingual data to build a high-performance translation model. For some language pairs, such as Chinese-English, massive bilingual data are available on the web. However, for most language pairs, large-scale bilingual data are hard to obtain. Pivot-based SMT is proposed to solve the data scarcity problem: it introduces a pivot language to bridge the source language and the target language. Therefore, a source-target translation model based on well-trained source-pivot and pivot-target translation models can be derived with the pivot-based approach. However, due to the ambiguities of the pivot language, source and target phrases with different meanings may be wrongly matched. Consequently, the derived source-target phrase table may contain incorrect phrase pairs. To alleviate this problem, we apply the MBR method to prune the phrase table. The MBR pruning method removes the phrase pairs with the lowest risk from the phrase table. Experimental results on Europarl data show that the proposed method can both reduce the size of phrase tables and improve the performance of translations. This study also gives a useful reference to many IoT research field and smart web services.

Word Sense Disambiguation Using Semantic Web for Tamil to English Statistical Machine Translation

<div><p><em> Machine Translation has been an area of linguistic research for almost more than two decades now. But it still remains a very challenging task for devising an automated system which will deliver accurate translations of the natural languages. However, great strides have been made in this field with more success owing to the development of technologies of the web and off late there is a renewed interest in this area of research. </em></p><p><em> Technological advancements in the preceding two decades have influenced Machine Translation in a considerable way. Several MT approaches including Statistical Machine Translation greatly benefitted from these advancements, basically making use of the availability of extensive corpora. Web technology web3.0 uses the semantic web technology which represents any object or resource in the web both syntactically and semantically. This type of representation is very much useful for the computing systems to search any content on the internet similar to lexical search and improve the internet based translations making it more effective and efficient.</em></p><p><em> In this paper we propose a technique to improve existing statistical Machine Translation methods by making use of semantic web technology. Our focus will be on Tamil and Tamil to English MT. The proposed method could successfully integrate a semantic web technique in the process of WSD which forms part of the MT system. The integration is accomplished by using the capabilities of RDFS and OWL into the WSD component of the MT model. The contribution of this work lies in showing that integrating a Semantic web technique in the WSD system significantly improves the performance of a statistical MT system for a translation from Tamil to English.</em></p></div><em> In this paper we assume the availability of large corpora in Tamil language and specific domain based ontologies with Tamil semantic web technology using web3.0. We are positive on the expansion and development of Tamil semantic web and subsequently infer that Tamil to English MT will greatly improve the disambiguation concept apart from other related benefits. This method could enable the enhancement of translation quality by improving on word sense disambiguation process while text is translated from Tamil to English language. This method can also be extended to other languages such as Hindi and Indian Languages.</em>

A Study of Statistical Machine Translation Methods for Under Resourced Languages

This paper contributes an empirical study of the application of five state-of-the-art machine translation to the trans- lation of low-resource languages. The methods studied were phrase-based, hierarchical phrase-based, the operational sequence model, string-to-tree, tree-to-string statistical machine translation methods between English (en) and the under resourced languages Lao (la), Myanmar (mm), Thai (th) in both directions. The performance of the machine translation systems was automatically measured in terms of BLEU and RIBES for all experiments. Our main findings were that the phrase-based SMT method generally gave the highest BLEU scores. This was counter to expectations, and we believe indicates that this method may be more robust to limitations on the data set size. However, when evaluated with RIBES, the best scores came from methods other than phrase-based SMT, indicating that the other methods were able to handle the word re-ordering better even under the constraint of limited data. Our study achieved the highest reported results on the data sets for all translation language pairs.

Transliteration normalization for Information Extraction and Machine Translation

Foreign name transliterations typically include multiple spelling variants. These variants cause data sparseness and inconsistency problems, increase the Out-of-Vocabulary (OOV) rate, and present challenges for Machine Translation, Information Extraction and other natural language processing (NLP) tasks. This work aims to identify and cluster name spelling variants using a Statistical Machine Translation method: word alignment. The variants are identified by being aligned to the same “pivot” name in another language (the source-language in Machine Translation settings). Based on word-to-word translation and transliteration probabilities, as well as the string edit distance metric, names with similar spellings in the target language are clustered and then normalized to a canonical form. With this approach, tens of thousands of high-precision name transliteration spelling variants are extracted from sentence-aligned bilingual corpora in Arabic and English (in both languages). When these normalized name spelling variants are applied to Information Extraction tasks, improvements over strong baseline systems are observed. When applied to Machine Translation tasks, a large improvement potential is shown.

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

A Novel Part-of-Speech Set Developing Method for Statistical Machine Translation

Part of speech (PoS) is one of the features that can be used to improve the quality of statistical-based machine translation. Typically, the language PoS determined based grammar of the language or adopt from other languages PoS. This work aims to formulate a model to developing PoS as linguistic factors to improve the quality of machine translation automatically. The research method using word similarity approach, where we perform clustering of the words contained in a corpus. Further classes will be defined as PoS set obtained for a given language.We evaluated the results of the PoS that defined computational results using machine translation system MOSES as the system by comparing the results of the SMT are using PoS sets generated manually, while the assessment of the system using BLEU method. Language that will be used for evaluation is English as the source language and Indonesian as the target language.

Mind the Gap: Machine Translation by Minimizing the Semantic Gap in Embedding Space

The conventional statistical machine translation (SMT) methods perform the decoding process by compositing a set of the translation rules which are associated with high probabilities. However, the probabilities of the translation rules are calculated only according to the cooccurrence statistics in the bilingual corpus rather than the semantic meaning similarity. In this paper, we propose a Recursive Neural Network (RNN) based model that converts each translation rule into a compact real-valued vector in the semantic embedding space and performs the decoding process by minimizing the semantic gap between the source language string and its translation candidates at each state in a bottom-up structure. The RNN-based translation model is trained using a max-margin objective function. Extensive experiments on Chinese-to-English translation show that our RNN-based model can significantly improve the translation quality by up to 1.68 BLEU score.

An Enhancement Method for Japanese-English Automated Translation

We present a method for improving existing statistical machine translation methods using a knowledge base compiled from a bilingual corpus as well as sequence alignment and pattern matching techniques from the area of machine learning and bioinformatics. An alignment algorithm identifies similar sentences, which are then used to construct a better word order for the translation. Our preliminary test results indicate a significant improvement of the translation quality.

Max Margin Learning for Statistical Machine Translation Toward Improvement of Machine Translation Accuracy

Minimum error rate training (MERT) has been a widely used learning method for statistical machine translation to estimate the feature function weights of a linear model. MERT has an advantage to incorpolate an automatic translation evaluation metrics as BLEU scores to its objective function. Weight vector can directly be optimized with Line search algorithm using error surface on a given set of candidate translations. It efficiently searches the best parameter resulting the highest BLEU scores. In this paper, we presented a new training algorithm for statisitcal machine translation, inspired by MERT and Structural Support Vector Machines. We performed MERT optimization by maximizing the margin between the oracle and incorrect translations under the L2-norm prior. Our experimental results on Japanese-English speech translation task showed that BLEU scores obtained by our proposed method were much better than those obtained by MERT. We achieved the best improvement of BLEU about +3.0 over standard MERT.

A process study of computer-aided translation

We investigate novel types of assistance for human translators, based on statistical machine translation methods. We developed the computer-aided tool Caitra that makes suggestions for sentence completion, shows word and phrase translation options, and allows postediting of machine translation output. We carried out a study of the translation process that involved non-professional translators that were native in either French or English and recorded their interaction with the tool. Users translated 192 sentences from French news stories into English. Most translators were faster and better when assisted by our tool. A detailed examination of the logs also provides insight into the human translation process, such as time spent on different activities and length of pauses.

Chinese-Uyghur machine translation system for phrase-based statistical translation

This paper described a Chinese-Uyghur machine translation system for phrase-based statistical translation.First the Chinese-Uyghur corpus was used to train the language model and the translation model.After using these models to decode the source statement,the authors got the best translation statement.The core decoding algorithm was the beam search algorithm,and the Latin-Script Uyghur was used as the Uyghur corpus.The experimental results show that phrase-based statistical machine translation method can be used quickly and effectively to build a Chinese-Uygher machine translation platform.

Double-tier Chinese-Mongolian machine translation based on statistical and template

To improve the accuracy of the translation of the Chinese-Mongolian language machine translation system,this paper proposed the methods that combined the phrase-based statistical machine translation and template based machine translation to automatically extract template structure.Template matching was the first step in decoding and the template structure was applied for translation,and then according to Beam Search algorithm a follow-up translation was carried out.Thus,the method can effectively resolve Word Order errors in a single statistical translation.Take a Chinese-Mongolian translation as an example,the experimental results show that this method can effectively improve translation effects.In the area of agricultural language Translation,agriculture phrase templates were added,and the translation efficiency increased by 10% than Och's phrase-based statistical machine translation methods.

Pivot language approach for phrase-based statistical machine translation

This paper proposes a novel method for phrase-based statistical machine translation based on the use of a pivot language. To translate between languages L s and L t with limited bilingual resources, we bring in a third language, L p , called the pivot language. For the language pairs L s − L p and L p − L t , there exist large bilingual corpora. Using only L s − L p and L p − L t bilingual corpora, we can build a translation model for L s − L t . The advantage of this method lies in the fact that we can perform translation between L s and L t even if there is no bilingual corpus available for this language pair. Using BLEU as a metric, our pivot language approach significantly outperforms the standard model trained on a small bilingual corpus. Moreover, with a small L s − L t bilingual corpus available, our method can further improve translation quality by using the additional L s − L p and L p − L t bilingual corpora.