Machine translation method using inductive learning with genetic algorithms

Echizen-ya, Hiroshi; Araki, Kenji; Momouchi, Yoshio; Tochinai, Koji

doi:10.3115/993268.993356

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…One of these works (Echizen-ya et al 1996) deals with translation rules learning, others are focused in sentence translation and alignment (Otto and Rojas 2004;Rodríguez et al 2006), while others are devoted to particular aspects related to statistical MT, such as the bilingual dictionary construction (Han 2001) or the parameter tuning of a translation system (Nabhan and Rafea 2005). Echizen-ya et al (1996) propose the use of a GA to acquire translation rules. The proposal amounts to using inductive learning with genetic algorithms and is applied to the process of English-Japanese translation.…”

Section: Machine Translationmentioning

confidence: 99%

How evolutionary algorithms are applied to statistical natural language processing

Araujo

2007

Artif Intell Rev

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Statistical natural language processing (NLP) and evolutionary algorithms (EAs) are two very active areas of research which have been combined many times. In general, statistical models applied to deal with NLP tasks require designing specific algorithms to be trained and applied to process new texts. The development of such algorithms may be hard. This makes EAs attractive since they offer a general design, yet providing a high performance in particular conditions of application. In this article, we present a survey of many works which apply EAs to different NLP problems, including syntactic and semantic analysis, grammar induction, summaries and text generation, document clustering and machine translation. This review finishes extracting conclusions about which are the best suited problems or particular aspects within those problems to be solved with an evolutionary algorithm.

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Section: Machine Translationmentioning

confidence: 99%

How evolutionary algorithms are applied to statistical natural language processing

Araujo

2007

Artif Intell Rev

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“…In the feedback process, the system evaluates the translation rules used. First, the system evaluates the translation rules without variables by using the results of combinations between the translation rules with variables and the translation rules without variables (Echizen-ya et al, 1996). Next, the system evaluates translation rules with variables by using the processes of combinations between the translations rules with variables and the translation rules without variables (Echizen-ya et al, 2000).…”

Section: Feedback Processmentioning

confidence: 99%

Study of practical effectiveness for machine translation using recursive chain-link-type learning

Echizen-ya

Momouchi

Araki

et al. 2002

Proceedings of the 19th International Conference on Computational Linguistics -

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A number of machine translation systems based on the learning algorithms are presented. These methods acquire translation rules from pairs of similar sentences in a bilingual text corpora. This means that it is difficult for the systems to acquire the translation rules from sparse data. As a result, these methods require large amounts of training data in order to acquire high-quality translation rules. To overcome this problem, we propose a method of machine translation using a Recursive Chain-linktype Learning. In our new method, the system can acquire many new high-quality translation rules from sparse translation examples based on already acquired translation rules. Therefore, acquisition of new translation rules results in the generation of more new translation rules. Such a process of acquisition of translation rules is like a linked chain. From the results of evaluation experiments, we confirmed the effectiveness of Recursive Chain-link-type Learning.

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“…In previous works, the genetic algorithm was proposed to handle some parts of the automatic translation. In [7] a genetic algorithm was used in a learning process to generate new translation examples, but for an example-based machine translation system and not for SMT. Another work [19] proposed an algorithm to generate a multi-word-based Translation Model, and to evaluate this model, a basic genetic algorithm was used as a translator.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Based Decoder for Statistical Machine Translation

Douib¹,

Langlois²,

Smaïli³

2018

Computational Linguistics and Intelligent Text Processing

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We propose a new algorithm for decoding on machine translation process. This approach is based on an evolutionary algorithm. We hope that this new method will constitute an alternative to Moses's decoder which is based on a beam search algorithm while the one we propose is based on the optimisation of a total solution. The results achieved are very encouraging in terms of measures and the proposed translations themselves are well built.

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Machine translation method using inductive learning with genetic algorithms

Cited by 9 publications

References 4 publications

How evolutionary algorithms are applied to statistical natural language processing

How evolutionary algorithms are applied to statistical natural language processing

Study of practical effectiveness for machine translation using recursive chain-link-type learning

Genetic-Based Decoder for Statistical Machine Translation

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