Machine Translation Optimization using Hybrid Architectures

This work is devoted to the description of the project of updating the technology of algorithmic language application created in the USSR in the 60s of the XX century by V. F. Turchin. The language was originally intended for various logical transformations primarily of text material. In practice, the scope was wider: machine translation, optimization and compilation of programs, proof of theorems, modeling of complex electronic circuits, solving a number of problems of artificial intelligence. The language now has a sufficient number of followers, mainly in scientific circles.The objective of the described project is to create a product that allows the use of Refal in modern mass applications and to expand the range of its potential users to the entire Internet. A survey of the community of users and developers of Refal was conducted in order to get an idea of the current state of Affairs, current implementations and ways of language development. Possible means of project implementation were considered. No information was received on similar developments under way.

Download Full-text

Artificial Intelligence Machine Translation Based on Fuzzy Algorithm

Mobile Information Systems ◽

10.1155/2021/1827627 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Zhimin Li

Keyword(s):

Artificial Intelligence ◽

Machine Translation ◽

Fault Tolerant ◽

Fuzzy Theory ◽

Analytic Hierarchy ◽

Fuzzy Algorithms ◽

The Neural Network ◽

Permutation Model ◽

Hierarchy Process ◽

Translation Optimization

In order to study machine translation more in-depth, it is particularly important for the research of artificial intelligence with fuzzy algorithms to convert an unfamiliar language into a mature language. The neural network translation model has been developed in recent years and has achieved rich research results. Aiming at the current lack of accuracy of neural machine translation (NMT), which may cause ambiguity, this paper takes English machine translation as an example and proposes an artificial intelligence machine translation optimization model based on fuzzy theory. On the basis of NMT model translation, first the semantics of English machine translation is classified, a semantic selection model is built, then the analytic hierarchy process is used to determine the semantic order of English machine translation, and the corresponding fault-tolerant operation is carried out to the error-prone errors, weight the semantics, and introduce the fuzzy theory to arrange the English semantics of English machine translation. Finally, the performance of the model is analyzed through specific application experiments. The results show that the accuracy of the machine translation selection permutation model is improved by nearly 4.5% and can reach more than 90% compared with other models, and the timeliness is better than other models, which is improved by nearly 15%, which has obvious advantages.

Download Full-text

SPSA vs Simplex in statistical machine translation optimization

PAMM ◽

10.1002/pamm.200700551 ◽

2007 ◽

Vol 7 (1) ◽

pp. 1062503-1062504 ◽

Cited By ~ 1

Author(s):

Patrik Lambert ◽

Rafael E. Banchs

Keyword(s):

Machine Translation ◽

Statistical Machine Translation ◽

Translation Optimization

Download Full-text

Factored Statistical Machine Translation for German-English

Journal of Applied Information, Communication and Technology ◽

10.33555/ejaict.v5i1.47 ◽

2018 ◽

Vol 5 (1) ◽

pp. 37-45

Author(s):

Darryl Yunus Sulistyan

Keyword(s):

Machine Translation ◽

English Language ◽

Statistical Machine Translation ◽

New Model ◽

Language Pair

Machine Translation is a machine that is going to automatically translate given sentences in a language to other particular language. This paper aims to test the effectiveness of a new model of machine translation which is factored machine translation. We compare the performance of the unfactored system as our baseline compared to the factored model in terms of BLEU score. We test the model in German-English language pair using Europarl corpus. The tools we are using is called MOSES. It is freely downloadable and use. We found, however, that the unfactored model scored over 24 in BLEU and outperforms the factored model which scored below 24 in BLEU for all cases. In terms of words being translated, however, all of factored models outperforms the unfactored model.

Download Full-text

On (Not) Translating Lacan: Barbara Cassin's Sophistico-Analytical Performances

Paragraph ◽

10.3366/para.2020.0323 ◽

2020 ◽

Vol 43 (1) ◽

pp. 98-113

Author(s):

Michael Syrotinski

Keyword(s):

Machine Translation ◽

Reading And Writing ◽

The Relationship ◽

The Way

Barbara Cassin's Jacques the Sophist: Lacan, Logos, and Psychoanalysis, recently translated into English, constitutes an important rereading of Lacan, and a sustained commentary not only on his interpretation of Greek philosophers, notably the Sophists, but more broadly the relationship between psychoanalysis and sophistry. In her study, Cassin draws out the sophistic elements of Lacan's own language, or the way that Lacan ‘philosophistizes’, as she puts it. This article focuses on the relation between Cassin's text and her better-known Dictionary of Untranslatables, and aims to show how and why both ‘untranslatability’ and ‘performativity’ become keys to understanding what this book is not only saying, but also doing. It ends with a series of reflections on machine translation, and how the intersubjective dynamic as theorized by Lacan might open up the possibility of what is here termed a ‘translatorly’ mode of reading and writing.

Download Full-text

A Review and evaluation of Machine Translation methods for Lumasaaba

Journal of Digital Science ◽

10.33847/2686-8296.2.1_1 ◽

2020 ◽

pp. 3-17

Author(s):

Peter Nabende

Keyword(s):

Natural Language Processing ◽

Natural Language ◽

Machine Translation ◽

Language Processing ◽

Research Area ◽

Data Driven ◽

East African ◽

Data Set ◽

African Languages ◽

Translation Methods

Natural Language Processing for under-resourced languages is now a mainstream research area. However, there are limited studies on Natural Language Processing applications for many indigenous East African languages. As a contribution to covering the current gap of knowledge, this paper focuses on evaluating the application of well-established machine translation methods for one heavily under-resourced indigenous East African language called Lumasaaba. Specifically, we review the most common machine translation methods in the context of Lumasaaba including both rule-based and data-driven methods. Then we apply a state of the art data-driven machine translation method to learn models for automating translation between Lumasaaba and English using a very limited data set of parallel sentences. Automatic evaluation results show that a transformer-based Neural Machine Translation model architecture leads to consistently better BLEU scores than the recurrent neural network-based models. Moreover, the automatically generated translations can be comprehended to a reasonable extent and are usually associated with the source language input.

Download Full-text