Rule-based Machine Translation System Research Articles

On the Concept of Translation Unit in a Machine Translation Framework

The article looks at the question whether the concept of translation unit might apply to the sphere of machine translation and whether the size of the unit influences the quality of translation. While modern machine translation systems offer an acceptable level of quality, a number of problems mainly related to the structural organization of the text remain unresolved, hence the question posed in the paper. The article offers a review of modern readings of the concept and pays special attention to the question whether the scope of the term changes depending on whether the object of research is the target text or the translation process. The paper also provides a quick look on the research methods for both text-oriented and process-oriented approaches, such as comparative analysis of language pairs and Think Aloud Protocol. Based on a review of existing machine translation models, each of them is analyzed to answer the question whether a unit of translation can be defined for a given system and what its size is. It is concluded that a unit of translation can be viewed as either a unit of analysis or a unit of processing with respect to text-oriented and process-oriented perspectives on to the study of translation. The unit of translation has a dynamic character and influences the quality of the target text. In machine translation, the unit of translation as a unit of analysis is not applicable for systems based on probabilistic non-linguistic methods. For rule-based machine translation systems, both readings of the unit of translation concept are applicable, but hardly go beyond a single sentence. Accordingly, at least one type of translation problem – intra-textual relations resolutions – remains largely unaddressed in the present state of affairs in machine translation.

Sentiment induced phrase-based machine translation: Robustness analysis of PBSMT with senti-module

Every type of machine translation system (i.e. neural, statistical, rule-based machine translation system) is equal important to build a sophistical hybrid machine translation system. Keeping this fact in my mind, I concentrate to improve statistical machine translation system with more natural way. In this paper, I try to preserve sentiment after translation to improve the overall accuracy of the machine translation system. So, I introduced senti-model here. A senti-model (sentiment model), translation model, language model, and distortion model are incorporated on the top of the beam search algorithm for decoding. At first, sentiment information is learned and modeled with translation probability by using this algorithm. Thereafter, I decode the source sentences-based on the contextual information. Overall procedure of translation modeling with a sentiment, parameter estimation for it, and senti-translation decoding (decoding with the sentiment model) are presented with empirical evidence. Experiments on a benchmark English–Hindi dataset shows that the proposed model is capable to improve the accuracy (in terms of 4.66 BLEU points, 4.09 LeBleu points, 4.67 NIST points, 5.71 RIBES points) significantly and preserves sentiment 7.79% more than the state-of-the-art technique.

Developing a Rule-Based Machine-Translation System, Ewondo–French–Ewondo

Machine translation (MT) significantly contributes to democratizing access to textual information across multiple languages and is established as a dynamic language service in the global multilingual society. Not surprisingly, the attractiveness of the MT market has stirred up spectacular innovations, driven by artificial intelligence in the digital technology industry. The commercial stakes of the industry has led to massive investments in the development of automatic translation systems for languages of wider communication and an increased marginalization of minority languages in this avenue. This article reports on the on-going development of a low-tech, rule-based MT system for Ewondo, a Bantu low-resourced language spoken in Cameroon. The project aims to fill the gap in access to MT services in the target minority language community and to generate parallel corpora from and into the Ewondo language.

Identifikasi Ekspresi Idiomatik Menggunakan Distributional Semantic Based Approach dan Truth Discovery

Idiomatic expressions are phrases that consist of a sequence of two or more words that have a meaning that cannot be predicted from the meaning of the individual words that compose it. Idiomatic expressions exist in almost all languages ??but are difficult to extract because there is no algorithm that can precisely decipher the structure of idiomatic expressions, so most rule-based machine translation systems generally translate idiomatic expressions by translating word for word their constituents, but the translation results do not produce the true meaning of the idiomatic expression. Based on this problem, the author tries to do research on the identification of the use of idiomatic expressions in Indonesian sentences. First, the author conducts the sentence classification process using BERT to find out whether the sentence contains idiomatic expressions or not. Furthermore, idiomatic expressions are identified based on distributional semantic based approach and then validated automatically using the Truth Discovery method. From the research conducted, the identification of idiomatic expressions in Indonesian sentences using Distributional Semantic Based Approach and Truth Discovery obtained an accuracy of 0.82; precision 1.0; recall 0.64 and f1-score 0.78.

Automatic Extraction of English-Chinese Translation Templates Based on Deep Learning

Translation templates are an important cause of knowledge in machine translation (MT) systems. Their quality and scale directly influence the performance of MT systems. How to obtain high-quality and efficient translation templates from corpora has become a hot topic in recent study. In this paper, a tree to String alignment template (TAT) based on syntactic structure is proposed. This template describes the alignment between the source language syntax tree and the target language string. The syntactic structure, a large number of construction tags, and variables are introduced into the template, which enables the syntactic model to deal with discontinuous phrases and has the ability of generalization. Templates can be used in syntactic statistics, case-based, and rule-based MT systems according to different decoders. ATTEBSC algorithm is a basic method to learn translation templates by comparing sentence pairs. It demands that sentence pairs be constructed in a precise comparison structure ahead of time, but there are no strict guidelines on how to do it. In this paper, we propose a method to calculate the specific comparison scheme using the longest common subsequence (LCS) and use the normalized LCS distance to screen sentences with high similarity and then use the ATTEBSC algorithm to automatically remove the template. Experiments show that this method is easy and effective, and many expensive templates can be learned.

Hva er viktig for forståelse? Om maskinoversetting fra nordsamisk

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.

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Introduction to the Special Issue on Evolving IoT and Cyber-Physical Systems: Advancements, Applications, and Solutions

Machine translation: a critical look at the performance of rule-based and statistical machine translation

O ensaio fornece uma avaliação crítica do desempenho de dois sistemas distintos de tradução automática, Systran e Google Translate. Primeiro, é fornecida uma breve visão geral dos sistemas de tradução automática baseados em regras e estatísticas, seguida de uma discussão sobre os problemas envolvidos na avaliação automática e humana das outputs de tradução automática. Por fim, as traduções em alemão de Mark Twain, traduzidas por Systran e Google Translate, estão sendo avaliadas criticamente, destacando alguns dos desafios linguísticos enfrentados por cada sistema de tradução.

A machine translation system from Arabic sign language to Arabic

Arabic sign language (ArSL) is one of the sign languages that is used in Arab countries. This language has structure and grammar that differ from spoken Arabic. Available ArSL recognition systems perform direct mapping between the recognized sign in the ArSL sentence and its corresponding Arabic word. This results in persevering the structure and grammar of the ArSL sentence. ArSL translation involves converting the recognized ArSL sentence into Arabic sentence that meets the structure and grammar of Arabic. We propose in this work a rule-based machine translation system between ArSL and Arabic. The proposed system performs morphological and syntactic analysis to translate the ArSL sentence lexically and syntactically into Arabic. To evaluate this work, we perform manual and automatic evaluation using a corpus on the health domain. The obtained results show that our translation system provides an accurate translation for more than 80% of the translated sentences.

English to Kurdish Rule-based Machine Translation System

In this paper we present a machine translation system developed to translate simple English sentences to Kurdish. The system is based on the (apertuim) free open source engine that provides the environment and the required tools to develop a machine translation system. The developed system is used to translate some as simple sentence, compound sentence, phrases and idioms from English to Kurdish. The resulting translation is then evaluated manually for accuracy and completeness compared to the result produced by the popular (inKurdish) English to Kurdish machine translation system. The result shows that our system is more accurate than inkurdish system. This paper contributes towards the ongoing effort to achieve full machine-based translation in general and English to Kurdish machine translation in specific.

Automatic translation of Arabic text-to-Arabic sign language

Arabic sign language (ArSL) is a full natural language that is used by the deaf in Arab countries to communicate in their community. Unfamiliarity with this language increases the isolation of deaf people from society. This language has a different structure, word order, and lexicon than Arabic. The translation between ArSL and Arabic is a complete machine translation challenge, because the two languages have different structures and grammars. In this work, we propose a rule-based machine translation system to translate Arabic text into ArSL. The proposed system performs a morphological, syntactic, and semantic analysis on an Arabic sentence to translate it into a sentence with the grammar and structure of ArSL. To transcribe ArSL, we propose a gloss system that can be used to represent ArSL. In addition, we develop a parallel corpus in the health domain, which consists of 600 sentences, and will be freely available for researchers. We evaluate our translation system on this corpus and find that our translation system provides an accurate translation for more than 80% of the translated sentences.

A novel rule based machine translation scheme from Greek to Greek Sign Language: Production of different types of large corpora and Language Models evaluation

One of the aims of assistive technologies is to help people with disabilities to communicate with others and to provide means of access to information. As an aid to Deaf people, in this work we present a novel prototype Rule Based Machine Translation (RBMT) system for the creation of large and quality written Greek Sign Language (GSL) glossed corpora from Greek text. In particular, the proposed RBMT system assists the professional GSL translator in speeding up the production of different kinds of GSL glossed corpora. Then each glossed corpus is used for the production/creation of Language Model (LM) n-grams. With the GSL glossed corpus from Greek text, we can build, test and evaluate different kinds of Language Models for different kinds of glossed GSL corpora. Here, it should be noted that it does not require grammar knowledge of GSL but only very basic GSL phenomena covered by manual RBMT rules as it assists the professional human translator. Furthermore, it should also be stressed that Language Models for written GSL gloss are missing from the scientific literature, thus this work is pioneer in this field. Evaluation of the proposed scheme is carried out for the weather reports domain, where 20,284 tokens and 1000 sentences have been produced. By using the BiLingual Evaluation Understudy (BLEU) metric score, our prototype RBMT system achieves a relative score of 0.84 (84%) for 4-grams and 0.9 (90%) for 1-grams.

Method to Overcome the Ambiguities in Shallow Parse and Transfer Machine Translation

The manuscript proposes a new architecture for a Shallow Parsing and Shallow Transfer Rule-Based Machine Translation System. The newly proposed architecture omits the disambiguation module in the starting phases of the translation pipeline and stores all translation candidates generated by the ambiguous process in the morphological analysis phase. The architecture is based on multigraphs. We propose a simplified version of k-best shortest path algorithm for this special case of directed multigraph. The k-best set is ranked using a trigram language model. The empirical evaluation shows that the new architecture produces better translation quality results with constant delay in time.

A Linguistic Evaluation of Rule-Based, Phrase-Based, and Neural MT Engines

AbstractIn this paper, we report an analysis of the strengths and weaknesses of several Machine Translation (MT) engines implementing the three most widely used paradigms. The analysis is based on a manually built test suite that comprises a large range of linguistic phenomena. Two main observations are on the one hand the striking improvement of an commercial online system when turning from a phrase-based to a neural engine and on the other hand that the successful translations of neural MT systems sometimes bear resemblance with the translations of a rule-based MT system.

A morphological analyser for Maltese

This article describes the development of a free/open-source morphological description of Maltese, originally created as the analysis component in a rule-based machine translation system for Maltese to Arabic and later applied to other tasks. The lexicon formalism we use is lttoolbox, part of the Apertium machine translation platform. An evaluation of the analyser shows that the coverage is adequate, at 84.90%, while precision is 92.5% on a large automatically annotated test set and 96.2% on a smaller hand-validated set.

A North Saami to South Saami Machine Translation Prototype

The paper describes a rule-based machine translation (MT) system from North to South Saami. The system is designed for a workflow where North Saami functions as pivot language in translation from Norwegian or Swedish. We envisage manual translation from Norwegian or Swedish to North Saami, and thereafter MT to South Saami. The system was aimed at a single domain, that of texts for use in school administration. We evaluated the system in terms of the quality of translations for postediting. Two out of three of the Norwegian to South Saami professional translators found the output of the system to be useful. The evaluation shows that it is possible to make a functioning rule-based system with a small transfer lexicon and a small number of rules and achieve results that are useful for a restricted domain, even if there are substantial differences b etween the languages.

Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation

We describe a hybridisation strategy whose objective is to integrate linguistic resources from shallow-transfer rule-based machine translation (RBMT) into phrase-based statistical machine translation (PBSMT). It basically consists of enriching the phrase table of a PBSMT system with bilingual phrase pairs matching transfer rules and dictionary entries from a shallow-transfer RBMT system. This new strategy takes advantage of how the linguistic resources are used by the RBMT system to segment the source-language sentences to be translated, and overcomes the limitations of existing hybrid approaches that treat the RBMT systems as a black box. Experimental results confirm that our approach delivers translations of higher quality than existing ones, and that it is specially useful when the parallel corpus available for training the SMT system is small or when translating out-of-domain texts that are well covered by the RBMT dictionaries. A combination of this approach with a recently proposed unsupervised shallow-transfer rule inference algorithm results in a significantly greater translation quality than that of a baseline PBSMT; in this case, the only hand-crafted resource used are the dictionaries commonly used in RBMT. Moreover, the translation quality achieved by the hybrid system built with automatically inferred rules is similar to that obtained by those built with hand-crafted rules.

Description of the Chinese-to-Spanish Rule-Based Machine Translation System Developed Using a Hybrid Combination of Human Annotation and Statistical Techniques

Two of the most popular Machine Translation (MT) paradigms are rule based (RBMT) and corpus based, which include the statistical systems (SMT). When scarce parallel corpus is available, RBMT becomes particularly attractive. This is the case of the Chinese--Spanish language pair. This article presents the first RBMT system for Chinese to Spanish. We describe a hybrid method for constructing this system taking advantage of available resources such as parallel corpora that are used to extract dictionaries and lexical and structural transfer rules. The final system is freely available online and open source. Although performance lags behind standard SMT systems for an in-domain test set, the results show that the RBMT’s coverage is competitive and it outperforms the SMT system in an out-of-domain test set. This RBMT system is available to the general public, it can be further enhanced, and it opens up the possibility of creating future hybrid MT systems.

A generalised alignment template formalism and its application to the inference of shallow-transfer machine translation rules from scarce bilingual corpora

Statistical and rule-based methods are complementary approaches to machine translation (MT) that have different strengths and weaknesses. This complementarity has, over the last few years, resulted in the consolidation of a growing interest in hybrid systems that combine both data-driven and linguistic approaches. In this paper, we address the situation in which the amount of bilingual resources that is available for a particular language pair is not sufficiently large to train a competitive statistical MT system, but the cost and slow development cycles of rule-based MT systems cannot be afforded either. In this context, we formalise a new method that uses scarce parallel corpora to automatically infer a set of shallow-transfer rules to be integrated into a rule-based MT system, thus avoiding the need for human experts to handcraft these rules.Our work is based on the alignment template approach to phrase-based statistical MT, but the definition of the alignment template is extended to encompass different generalisation levels. It is also greatly inspired by the work of Sánchez-Martínez and Forcada (2009) in which alignment templates were also considered for shallow-transfer rule inference. However, our approach overcomes many relevant limitations of that work, principally those related to the inability to find the correct generalisation level for the alignment templates, and to select the subset of alignment templates that ensures an adequate segmentation of the input sentences by the rules eventually obtained. Unlike previous approaches in literature, our formalism does not require linguistic knowledge about the languages involved in the translation. Moreover, it is the first time that conflicts between rules are resolved by choosing the most appropriate ones according to a global minimisation function rather than proceeding in a pairwise greedy fashion.Experiments conducted using five different language pairs with the free/open-source rule-based MT platform Apertium show that translation quality significantly improves when compared to the method proposed by Sánchez-Martínez and Forcada (2009), and is close to that obtained using handcrafted rules. For some language pairs, our approach is even able to outperform them. Moreover, the resulting number of rules is considerably smaller, which eases human revision and maintenance.

English to Telugu Rule based Machine Translation System: A Hybrid Approach

This paper deals with adaptive rule based machine translation from English to Telugu. This approach is based on rule-based methodologies. If-then methods to select the best rules for target language in translation, Probability based appropriate word selection for a given sentence and rough sets to classify a given sentence are the approaches used in this technique. Set of production rules of English and Telugu, Training set and Dictionary for both the languages are developed for this purpose. User gives and input, which is an English sentence. The given input sentence is then tokenized into individual words. These words are tagged with their respective parts of speech. All other words that are not found in the pre-defined database are tagged using grammatical rules that are formulated. Using these POS tags, the respective word translations are retrieved from the database. These individual words are then concatenated to form a sentence that is the result of user’s input. General Terms Artificial Intelligence, Machine Learning, Intelligent Communicating Systems.