scholarly journals Prerequisites for Shallow-Transfer Machine Translation of Mordvin Languages: Language Documentation with a Purpose

Author(s):  
Jack Rueter ◽  
Mika Hämäläinen

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.

2016 ◽  
Vol 6 (1) ◽  
pp. 46-62
Author(s):  
Pramod P. Sukhadeve

Over the years, researches in machine translation (MT) systems have gain momentum due to their widespread applicability. A number of systems have come up doing the task successfully for different language pairs. However, to the best of the author's knowledge, no significant work has been done in clinical and medical related domain especially in Homoeopathy. This paper describes a rule based English-Hindi MT system for Homoeopathic sentences. It has been designed to translate a variety of sentences from Homoeopathic literature. To achieve the task, the author developed English and Hindi Homoeopathic corpuses presently having the size 21096 and 23145 sentences respectively. For translation, the input sentences (in English) have been categorised in four different type's i.e. simple, complex, interrogative and ambiguous sentences. The authors tested the translation accuracy using BLEU score. At present, the overall Bleu score of the system is 0.7808 and the accuracy percentage is 82.25%.


2021 ◽  
Vol 11 (2) ◽  
pp. 489-501
Author(s):  
Trond Trosterud ◽  
Lene Antonsen

The article presents a rule-based machine translation system from Northern Sami to Norwegian. The grammatical analysis is done with Giellatekno and Divvun's North Sami program for analysis and translation. We have written the transfer component (transfer lexicon and grammatical rules) within the framework of the open machine translation system Apertium. The article contains an evaluation of translated text for two different domains. The translated texts score better on the presentation of the content than on fluent language. By classifying the errors into lexical, grammatical and pragmatic errors, we show that lexical errors are the most harmful for text comprehension. The other two types of errors give a poor language quality, but they have little effect on comprehension. The type of error that is the easiest to correct is the lexical, which is a promising conclusion for the development of a machine translation system for text comprehension.


2017 ◽  
Vol 108 (1) ◽  
pp. 221-232
Author(s):  
Francis M. Tyers ◽  
Hèctor Alòs i Font ◽  
Gianfranco Fronteddu ◽  
Adrià Martín-Mor

AbstractThis paper describes the process of creation of the first machine translation system from Italian to Sardinian, a Romance language spoken on the island of Sardinia in the Mediterranean. The project was carried out by a team of translators and computational linguists. The article focuses on the technology used (Rule-Based Machine Translation) and on some of the rules created, as well as on the orthographic model used for Sardinian.


2016 ◽  
Vol 106 (1) ◽  
pp. 159-168 ◽  
Author(s):  
Julian Hitschler ◽  
Laura Jehl ◽  
Sariya Karimova ◽  
Mayumi Ohta ◽  
Benjamin Körner ◽  
...  

Abstract We present Otedama, a fast, open-source tool for rule-based syntactic pre-ordering, a well established technique in statistical machine translation. Otedama implements both a learner for pre-ordering rules, as well as a component for applying these rules to parsed sentences. Our system is compatible with several external parsers and capable of accommodating many source and all target languages in any machine translation paradigm which uses parallel training data. We demonstrate improvements on a patent translation task over a state-of-the-art English-Japanese hierarchical phrase-based machine translation system. We compare Otedama with an existing syntax-based pre-ordering system, showing comparable translation performance at a runtime speedup of a factor of 4.5-10.


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