Using Statistical Machine Translation Model to Improve Domain-Specific Metasearch Engines

2007 ◽  
Author(s):  
Kunhui Lin
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation Model ◽  
Domain Specific

A Topic-Triggered Translation Model for Statistical Machine Translation

2017 ◽  
Vol 26 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Jinsong Su ◽  
Zhihao Wang ◽  
Qingqiang Wu ◽  
Junfeng Yao ◽  
Fei Long ◽  
...  
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Translation Model

Aspects of Multilingual News Summarisation

2014 ◽  
pp. 277-294
Author(s):  
Josef Steinberger ◽  
Ralf Steinberger ◽  
Hristo Tanev ◽  
Vanni Zavarella ◽  
Marco Turchi
Keyword(s):  
Machine Translation ◽  
Latent Semantic Analysis ◽  
Automatic System ◽  
Semantic Analysis ◽  
Statistical Machine Translation ◽  
Challenging Problem ◽  
Parallel Corpus ◽  
High Performing ◽  
Domain Specific ◽  
Multiple Languages

In this chapter, the authors discuss several pertinent aspects of an automatic system that generates summaries in multiple languages for sets of topic-related news articles (multilingual multi-document summarisation), gathered by news aggregation systems. The discussion follows a framework based on Latent Semantic Analysis (LSA) because LSA was shown to be a high-performing method across many different languages. Starting from a sentence-extractive approach, the authors show how domain-specific aspects can be used and how a compression and paraphrasing method can be plugged in. They also discuss the challenging problem of summarisation evaluation in different languages. In particular, the authors describe two approaches: the first uses a parallel corpus and the second statistical machine translation.

Template-Based Model for Mongolian-Chinese Machine Translation

2016 ◽  
Vol 20 (6) ◽  
pp. 893-901
Author(s):  
Jing Wu ◽  
◽  
Hongxu Hou ◽  
Feilong Bao ◽  
Yupeng Jiang
Keyword(s):  
Machine Translation ◽  
Statistical Machine Translation ◽  
Combined System ◽  
Translation Model ◽  
Parallel Corpus ◽  
Address Data ◽  
Fuzzy Match ◽  
Novel Method ◽  
Extraction Model ◽  
Template Extraction

Mongolian and Chinese statistical machine translation (SMT) system has its limitation because of the complex Mongolian morphology, scarce resource of parallel corpus and the significant syntax differences. To address these problems, we propose a template-based machine translation (TBMT) system and combine it with the SMT system to achieve a better translation performance. The TBMT model we proposed includes a template extraction model and a template translation model. In the template extraction model, we present a novel method of aligning and abstracting static words from bilingual parallel corpus to extract templates automatically. In the template translation model, our specially designed method of filtering out the low quality matches can enhance the translation performance. Moreover, we apply lemmatization and Latinization to address data sparsity and do the fuzzy match. Experimentally, the coverage of TBMT system is over 50%. The combined SMT system translates all the other uncovered source sentences. The TBMT system outperforms the baselines of phrase-based and hierarchical phrase-based SMT systems for +3.08 and +1.40 BLEU points. The combined system of TBMT and SMT systems also performs better than the baselines of +2.49 and +0.81 BLEU points.

scholarly journals The Alignment Template Approach to Statistical Machine Translation

2004 ◽  
Vol 30 (4) ◽  
pp. 417-449 ◽  
Author(s):  
Franz Josef Och ◽  
Hermann Ney
Keyword(s):  
Machine Translation ◽  
Word Order ◽  
Search Algorithm ◽  
Statistical Machine Translation ◽  
Target Language ◽  
Linear Modeling ◽  
Translation Model ◽  
Log Linear ◽  
Translation Systems ◽  
English Canadian

A phrase-based statistical machine translation approach — the alignment template approach — is described. This translation approach allows for general many-to-many relations between words. Thereby, the context of words is taken into account in the translation model, and local changes in word order from source to target language can be learned explicitly. The model is described using a log-linear modeling approach, which is a generalization of the often used source-channel approach. Thereby, the model is easier to extend than classical statistical machine translation systems. We describe in detail the process for learning phrasal translations, the feature functions used, and the search algorithm. The evaluation of this approach is performed on three different tasks. For the German-English speech Verbmobil task, we analyze the effect of various system components. On the French-English Canadian Hansards task, the alignment template system obtains significantly better results than a single-word-based translation model. In the Chinese-English 2002 National Institute of Standards and Technology (NIST) machine translation evaluation it yields statistically significantly better NIST scores than all competing research and commercial translation systems.

scholarly journals Translation Mechanism of Neural Machine Algorithm for Online English Resources

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yanping Ye
Keyword(s):  
Data Processing ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Processing Methods ◽  
Neural Machine Translation ◽  
Translation Model ◽  
Sentence Similarity ◽  
Alignment Structure ◽  
Data Processing Methods

At the level of English resource vocabulary, due to the lack of vocabulary alignment structure, the translation of neural machine translation has the problem of unfaithfulness. This paper proposes a framework that integrates vocabulary alignment structure for neural machine translation at the vocabulary level. Under the proposed framework, the neural machine translation decoder receives external vocabulary alignment information during each step of the decoding process to further alleviate the problem of missing vocabulary alignment structure. Specifically, this article uses the word alignment structure of statistical machine translation as the external vocabulary alignment information and introduces it into the decoding step of neural machine translation. The model is mainly based on neural machine translation, and the statistical machine translation vocabulary alignment structure is integrated on the basis of neural networks and continuous expression of words. In the model decoding stage, the statistical machine translation system provides appropriate vocabulary alignment information based on the decoding information of the neural machine translation and recommends vocabulary based on the vocabulary alignment information to guide the neural machine translation decoder to more accurately estimate its vocabulary in the target language. From the aspects of data processing methods and machine translation technology, experiments are carried out to compare the data processing methods based on language model and sentence similarity and the effectiveness of machine translation models based on fusion principles. Comparative experiment results show that the data processing method based on language model and sentence similarity effectively guarantees data quality and indirectly improves the algorithm performance of machine translation model; the translation effect of neural machine translation model integrated with statistical machine translation vocabulary alignment structure is compared with other models.

scholarly journals Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation

2010 ◽  
Vol 93 (1) ◽  
pp. 17-26 ◽  
Author(s):  
Yvette Graham
Keyword(s):  
Open Source ◽  
Linear Combination ◽  
Machine Translation ◽  
Statistical Machine Translation ◽  
Language Model ◽  
Beam Search ◽  
Translation Model ◽  
Transfer Rules ◽  
Log Linear

Sulis: An Open Source Transfer Decoder for Deep Syntactic Statistical Machine Translation In this paper, we describe an open source transfer decoder for Deep Syntactic Transfer-Based Statistical Machine Translation. Transfer decoding involves the application of transfer rules to a SL structure. The N-best TL structures are found via a beam search of TL hypothesis structures which are ranked via a log-linear combination of feature scores, such as translation model and dependency-based language model.

scholarly journals N-gram-based Machine Translation

2006 ◽  
Vol 32 (4) ◽  
pp. 527-549 ◽  
Author(s):  
José B. Mariño ◽  
Rafael E. Banchs ◽  
Josep M. Crego ◽  
Adrià de Gispert ◽  
Patrik Lambert ◽  
...  
Keyword(s):  
Linear Combination ◽  
Machine Translation ◽  
State Of The Art ◽  
Statistical Machine Translation ◽  
European Parliament ◽  
The State ◽  
Translation Model ◽  
Model Based ◽  
N Gram ◽  
Log Linear

This article describes in detail an n-gram approach to statistical machine translation. This approach consists of a log-linear combination of a translation model based on n-grams of bilingual units, which are referred to as tuples, along with four specific feature functions. Translation performance, which happens to be in the state of the art, is demonstrated with Spanish-to-English and English-to-Spanish translations of the European Parliament Plenary Sessions (EPPS).

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