Quantum Neural Network Based Machine Translator for Hindi to English

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation

Interlingual Machine Translation

Interlingual is an artificial language used to represent the meaning of natural languages, as for purposes of machine translation. It is an intermediate form between two or more languages. Machine translation is the process of translating from source language text into the target language. This paper proposes a new model of machine translation system in which rule-based and example-based approaches are applied for English-to-Kannada/Telugu sentence translation. The proposed method has 4 steps: 1) analyze an English sentence into a string of grammatical nodes, based on Phrase Structure Grammar, 2) map the input pattern with a table of English-Kannada/Telugu sentence patterns, 3) look up the bilingual dictionary for the equivalent Kannada/Telugu words, reorder and then generate output sentences and 4) rank the possible combinations and eliminate the ambiguous output sentences by using a statistical method. The translated sentences will then be stored in a bilingual corpus to serve as a guide or template for imitating the translation, i.e., the example-based approach. The future work will focus on sentence translation by using semantic features to make a more precise translation

Online adaptation strategies for statistical machine translation in post-editing scenarios

[EN] One of the most promising approaches to machine translation consists in formulating the problem by means of a pattern recognition approach. By doing so, there are some tasks in which online adapta- tion is needed in order to adapt the system to changing scenarios. In the present work, we perform an exhaustive comparison of four online learning algorithms when combined with two adaptation strategies for the task of online adaptation in statistical machine translation. Two of these algorithms are already well-known in the pattern recognition community, such as the perceptron and passive- aggressive algorithms, but here they are thoroughly analyzed for their applicability in the statistical machine translation task. In addition, we also compare them with two novel methods, i.e., Bayesian predictive adaptation and discriminative ridge regression. In statistical machine translation, the most successful approach is based on a log-linear approximation to a posteriori distribution. According to experimental results, adapting the scaling factors of this log-linear combination of models using discriminative ridge regression or Bayesian predictive adaptation yields the best performance.This paper is based upon work supported by the EC (FP7) under CasMaCat (287576) project and the EC (FEDER/FSE) and the Spanish MICINN under projects MIPRCV "Consolider Ingenio 2010" (CSD2007-00018) and iTrans2 (TIN2009-14511). This work is also supported by the Spanish MITyC under the erudito.com (TSI-020110-2009-439) project, by the Generalitat Valenciana under Grant Prometeo/2009/014, and by the UPV under Grant 20091027. The authors would like to thank the anonymous reviewers for their useful and constructive comments.Martínez Gómez, P.; Sanchis Trilles, G.; Casacuberta Nolla, F. (2012). Online adaptation strategies for statistical machine translation in post-editing scenarios. Pattern Recognition. 45(9):3193-3203. https://doi.org/10.1016/j.patcog.2012.01.011S3193320345

Pattern-Based Machine Translation of Technical Documentation

Переклад на основі шаблонів — це нова модель машинного перекладу, яка використовується для перекладу технічної документації. Вона використовує обмежену кількість шаблонів без будь-якої істотної втрати значення. Патерн — це семантична і синтаксична конструкція, де семантичний компонент відіграє домінуючу роль, а синтаксичні компоненти є змінними.Pattern-based translation is a new model of machine translation which is used to translate technical documentation. It uses a limited number of patterns without any substantial loss in meaning. A pattern is a semantic and syntactic construction where a semantic component plays a dominant part and syntactic components are variables