Rule Based Case Transfer in Tamil-Malayalam Machine Translation

AbstractChemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.

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English to Sanskrit machine translation system: a rule-based approach

International Journal of Advanced Intelligence Paradigms ◽

10.1504/ijaip.2012.048144 ◽

2012 ◽

Vol 4 (2) ◽

pp. 168 ◽

Cited By ~ 2

Author(s):

Vimal Mishra ◽

R.B. Mishra

Keyword(s):

Machine Translation ◽

Translation System ◽

Rule Based ◽

System A ◽

Machine Translation System ◽

Rule Based Approach

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Methods for integrating rule-based and statistical systems for Arabic to English machine translation

Machine Translation ◽

10.1007/s10590-011-9106-9 ◽

2011 ◽

Vol 26 (1-2) ◽

pp. 67-83 ◽

Cited By ~ 3

Author(s):

Rabih Zbib ◽

Michael Kayser ◽

Spyros Matsoukas ◽

John Makhoul ◽

Hazem Nader ◽

...

Keyword(s):

Machine Translation ◽

Rule Based ◽

Statistical Systems

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Adabot: Fault-Tolerant Java Decompiler (Student Abstract)

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i10.7203 ◽

2020 ◽

Vol 34 (10) ◽

pp. 13861-13862

Author(s):

Zhiming Li ◽

Qing Wu ◽

Kun Qian

Keyword(s):

Reverse Engineering ◽

Machine Translation ◽

Fault Tolerant ◽

Safety Concern ◽

Statistical Machine Translation ◽

Abstract Syntax ◽

Rule Based ◽

Abstract Syntax Tree ◽

Important Field ◽

Internal Architecture

Reverse Engineering has been an extremely important field in software engineering, it helps us to better understand and analyze the internal architecture and interrealtions of executables. Classical Java reverse engineering task includes disassembly and decompilation. Traditional Abstract Syntax Tree (AST) based disassemblers and decompilers are strictly rule defined and thus highly fault intolerant when bytecode obfuscation were introduced for safety concern. In this work, we view decompilation as a statistical machine translation task and propose a decompilation framework which is fully based on self-attention mechanism. Through better adaption to the linguistic uniqueness of bytecode, our model fully outperforms rule-based models and previous works based on recurrence mechanism.

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Recent advances in Apertium, a free/open-source rule-based machine translation platform for low-resource languages

Machine Translation ◽

10.1007/s10590-021-09260-6 ◽

2021 ◽

Author(s):

Tanmai Khanna ◽

Jonathan N. Washington ◽

Francis M. Tyers ◽

Sevilay Bayatlı ◽

Daniel G. Swanson ◽

...

Keyword(s):

Open Source ◽

Machine Translation ◽

Lexical Selection ◽

Rule Based ◽

Low Resource ◽

Language Technology ◽

Language Data ◽

Recursive Structures ◽

Platform Translation ◽

Free Open Source

AbstractThis paper presents an overview of Apertium, a free and open-source rule-based machine translation platform. Translation in Apertium happens through a pipeline of modular tools, and the platform continues to be improved as more language pairs are added. Several advances have been implemented since the last publication, including some new optional modules: a module that allows rules to process recursive structures at the structural transfer stage, a module that deals with contiguous and discontiguous multi-word expressions, and a module that resolves anaphora to aid translation. Also highlighted is the hybridisation of Apertium through statistical modules that augment the pipeline, and statistical methods that augment existing modules. This includes morphological disambiguation, weighted structural transfer, and lexical selection modules that learn from limited data. The paper also discusses how a platform like Apertium can be a critical part of access to language technology for so-called low-resource languages, which might be ignored or deemed unapproachable by popular corpus-based translation technologies. Finally, the paper presents some of the released and unreleased language pairs, concluding with a brief look at some supplementary Apertium tools that prove valuable to users as well as language developers. All Apertium-related code, including language data, is free/open-source and available at https://github.com/apertium.

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The Beginnings of Machine Translation: The First Rule Based Systems

Machine Translation ◽

10.7551/mitpress/11043.003.0008 ◽

2017 ◽

Keyword(s):

Machine Translation ◽

Rule Based ◽

Rule Based Systems

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English to Hindi Machine Translation System in the Context of Homoeopathy Literature

International Journal of Artificial Life Research ◽

10.4018/ijalr.2016010103 ◽

2016 ◽

Vol 6 (1) ◽

pp. 46-62

Author(s):

Pramod P. Sukhadeve

Keyword(s):

Machine Translation ◽

Translation System ◽

Simple Complex ◽

Rule Based ◽

Machine Translation System ◽

Translation Accuracy

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%.

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Function words in statistical machine-translated Chinese and original Chinese: A study into the translationese of machine translation systems

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy050 ◽

2018 ◽

Vol 34 (4) ◽

pp. 752-771

Author(s):

Chen-li Kuo

Keyword(s):

Machine Translation ◽

Attribute Selection ◽

Close Attention ◽

Function Words ◽

Rule Based ◽

Source Language ◽

Statistical Mt ◽

Chinese Texts ◽

Translation Systems

Abstract Statistical approaches have become the mainstream in machine translation (MT), for their potential in producing less rigid and more natural translations than rule-based approaches. However, on closer examination, the uses of function words between statistical machine-translated Chinese and the original Chinese are different, and such differences may be associated with translationese as discussed in translation studies. This article examines the distribution of Chinese function words in a comparable corpus consisting of MTs and the original Chinese texts extracted from Wikipedia. An attribute selection technique is used to investigate which types of function words are significant in discriminating between statistical machine-translated Chinese and the original texts. The results show that statistical MT overuses the most frequent function words, even when alternatives exist. To improve the quality of the end product, developers of MT should pay close attention to modelling Chinese conjunctions and adverbial function words. The results also suggest that machine-translated Chinese shares some characteristics with human-translated texts, including normalization and being influenced by the source language; however, machine-translated texts do not exhibit other characteristics of translationese such as explicitation.

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