Hybrid Machine Translation System Research Articles

Sanskrit to Hindi language translation using multimodal neural machine translation

Machine translation (MT) is a subfield of computer features that focuses on the automatic translation from one natural language into another without any human involvement. Due to native people interacting in a variety of languages, there is a great need for translating information between languages to send and communicate thoughts. However, they disregard the significance of semantic data encoded in the text features. In this paper, multimodal neural machine translation (MNMT) is proposed for Sanskrit-Hindi translation. The main goal of the proposed method is to fully utilize semantic text features on NMT architecture and to minimize testing and training time. The MNMT is validated on two different NMT architectures: recurrent neural network (RNN) and self-attention network (SAN). The MNMT method’s efficacy is demonstrated by employing the dataset of Sanskrit-Hindi Corpora. Extensive experimental outcomes represent the proposed method’s enhancement over baselines on both architectures. The existing methods, namely, English-to-Indian MT system, Sanskrit-Hindi MT system, and hybrid MT system are used to justify the efficacy of the MNMT method. When compared to the above-mentioned existing methods, RA-RNN respectively achieves a superior BLEU and METEOR of 80.5% and 75.3%, while the RA-SAN respectively achieves a superior BLEU and METEOR of 78.2% and 77.1%.

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.

Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation

This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian–English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.

Scaling up a Hybrid MT System: From low to full resources

This article describes a hybrid approach to machine translation (MT) that is inspired by the rule-based, statistical, example-based, and other hybrid machine translation approaches currently used or described in academic literature. It describes how the approach was implemented for language pairs using only limited monolingual resources and hardly any parallel resources (the METIS-II system), and how it is currently implemented with rich resources on both the source and target side as well as rich parallel data (the PaCo-MT system). We aim to illustrate that a similar paradigm can be used, irrespectively of the resources available, but of course with an impact on translation quality.

Adaptation in Statistical Machine Translation for Low-resource Domains in English-Vietnamese Language

Adaptation in Statistical Machine Translation for Low-resource Domains in English-Vietnamese Language

Evaluation of the impact of controlled language on neural machine translation compared to other MT architectures

Many studies have shown that the application of controlled languages (CL) is an effective pre-editing technique to improve machine translation (MT) output. In this paper, we investigate whether this also holds true for neural machine translation (NMT). We compare the impact of applying nine CL rules on the quality of NMT output as opposed to that of rule-based, statistical, and hybrid MT by applying three methods: error annotation, human evaluation, and automatic evaluation. The analyzed data is a German corpus-based test suite of technical texts that have been translated into English by five MT systems (a neural, a rule-based, a statistical, and two hybrid MT systems). The comparison is conducted in terms of several quantitative parameters (number of errors, error types, quality ratings, and automatic evaluation metrics scores). The results show that CL rules positively affect rule-based, statistical, and hybrid MT systems. However, CL does not improve the results of the NMT system. The output of the neural system is mostly error-free both before and after CL application and has the highest quality in both scenarios among the analyzed MT systems showing a decrease in quality after applying the CL rules. The qualitative discussion of the NMT output sheds light on the problems that CL causes for this kind of MT architecture.

English-Arabic Hybrid Machine Translation System using EBMT and Translation Memory

The availability of a machine translation to translate from English-to-Arabic with high accuracy is not available because of the difficult morphology of the Arabic Language. A hybrid machine translation system between Example Based machine translation technique and Translation memory was introduced in this paper. Two datasets have been used in the experiments that were constructed by using internal medicine publications and Worldwide Arabic Medical Translation Guide Common Medical Terms sorted by Arabic. To examine the accuracy of the system constructed four experiments were made using Example Based Machine Translation system in the first, Google Translate in the second and Example Based with Google translate in the third and the fourth is the system proposed using Example Based with Translation memory. The system constructed achieved 77.17 score for the first dataset and 63.85 score for the second which were the highest score using BLEU score.

Pause-Based Phrase Extraction and Effective OOV Handling for Low-Resource Machine Translation Systems

Machine translation is the core problem for several natural language processing research across the globe. However, building a translation system involving low-resource languages remains a challenge with respect to statistical machine translation (SMT). This work proposes and studies the effect of a phrase-induced hybrid machine translation system for translation from English to Tamil, under a low-resource setting. Unlike conventional hybrid MT systems, the free-word ordering feature of the target language Tamil is exploited to form a re-ordered target language model and to extend the parallel text corpus for training the SMT. In the current work, a novel rule-based phrase-extraction method, implemented using parts-of-speech (POS) and place-of-pause in both languages is proposed, which is used to pre-process the training corpus for developing the back-off phrase-induced SMT. Further, out-of-vocabulary (OOV) words are handled using speech-based transliteration and two-level thesaurus intersection techniques based on the POS tag of the OOV word. To ensure that the input with OOV words does not skip phrase-level translation in the hierarchical model, a phrase-level example-based machine translation approach is adopted to find the closest matching phrase and perform translation followed by OOV replacement. The proposed system results in a bilingual evaluation understudy score of 84.78 and a translation edit rate of 19.12. The performance of the system is compared in terms of adequacy and fluency, with existing translation systems for this specific language pair, and it is observed that the proposed system outperforms its counterparts.

An online English-Khmer hybrid machine translation system

The present paper describes design of an online hybrid machine translation (MT) system involving a low-resource language Khmer, the official language of Cambodia. The proposed system uses an open source statistical machine translation (SMT) toolkit DoMY CE as the primary translation tool. The parallel corpora have been prepared from various sources and the Headley Khmer-English dictionary. Language model, translation model and decoder configurations have been done using the DOMY toolkit. We used a post-processing step of using parts of speech tagger to enhance the quality of target language sentence. Experimental results demonstrate the success of the proposed scheme with English as source and Khmer as the target language. In our experiments the proposed model achieved significantly good National Institute of Standards and Technology (NIST) and BiLingual Evaluation Understudy (BLEU) scores. Different web technologies have been used for developing an online translation system.

An online English-Khmer hybrid machine translation system

The present paper describes design of an online hybrid machine translation (MT) system involving a low-resource language Khmer, the official language of Cambodia. The proposed system uses an open source statistical machine translation (SMT) toolkit DoMY CE as the primary translation tool. The parallel corpora have been prepared from various sources and the Headley Khmer-English dictionary. Language model, translation model and decoder configurations have been done using the DOMY toolkit. We used a post-processing step of using parts of speech tagger to enhance the quality of target language sentence. Experimental results demonstrate the success of the proposed scheme with English as source and Khmer as the target language. In our experiments the proposed model achieved significantly good National Institute of Standards and Technology (NIST) and BiLingual Evaluation Understudy (BLEU) scores. Different web technologies have been used for developing an online translation system.

Students’ Common Mistakes in Technical Texts When Using Computer Technologies

The paper considers the possibilities of using computer technologies by students in the translation of scientific and technical texts on power and electrical engineering. The main task of the study was to identify advantages and disadvantages of machine or computer-aided translation through their comparison and to define the possibility of their use in the translation of texts on power and electrical engineering. Among other types, students and post-graduates prefer machine translation. The comparison of two the most popular machine translation systems on the basis of Google Translate statistics and PROMT rules texts on power and electrical engineering showed that the Google Translate gives the better quality translation of special terms of this subject-matter. The majority of participants of the students’ international competition on written translation of the CIGRE Russian National Committee use Google Translate and PROMT. Translation mistakes are the result of insufficient language training and indistinct understanding of the described technical process. When translating written texts students shall apply post-editing and use the electronic dictionary Multitran. The development of hybrid machine translation systems on the basis of rules and statistics will allow shifting to a qualitatively new level of technical translation. The quality of students’ translations significantly increases if they participate in international and domestic competitions and get a profound knowledge of specialized subjects.

Speech translation system for english to dravidian languages

In this paper the Speech-to-Speech Translation (SST) system, which is mainly focused on translation from English to Dravidian languages (Tamil and Malayalam) has been proposed. Three major techniques involved in SST system are Automatic continuous speech recognition, machine translation, and text-to-speech synthesis system. In this paper automatic Continuous Speech Recognition (CSR) has been developed based on the Auto Associative Neural Network (AANN), Support Vector Machine (SVM) and Hidden Markov Model (HMM). The HMM yields better results compared with SVM and AANN. Hence the HMM based Speech recognizer for English language has been taken. We propose a hybrid Machine Translation (MT) system (combination of Rule based and Statistical) for converting English to Dravidian languages text. A syllable based concatenative Text To Speech Synthesis (TTS) for Tamil and Malayalam has been proposed. AANN based prosody prediction has been done for the Tamil language which is used to improve the naturalness and intelligibility. The domain is restricted to sentences that cover the announcements in the railway station, bus stop and airport. This work is framed a novel translation method for English to Dravidian languages. The improved performance of each module HMM based CSR, Hybrid MT and concatenative TTS increases the overall speech translation performance. This proposed speech translation system can be applied to English to any Indian languages if we train and create a parallel corpus for those languages.

Rule Based and Expectation Maximization algorithm for Arabic-English Hybrid Machine Translation

<p><strong> </strong>It is practically impossible for pure machine translation approach to process all of translation problems; however, Rule Based Machine Translation and Statistical Machine translation (RBMT and SMT) use different architectures for performing translation task. Lexical analyser and syntactic analyser are solved by Rule Based and some amount of ambiguity is left to be solved by Expectation–Maximization (EM) algorithm, which is an iterative statistic algorithm for finding maximum likelihood. In this paper we have proposed an integrated Hybrid Machine Translation (HMT) system. The goal is to combine the best properties of each approach. Initially, Arabic text is keyed into RBMT; then the output will be edited by EM algorithm to generate the final translation of English text. As we have seen in previous works, the performance and enhancement of EM algorithm, the key of EM algorithm performance is the ability to accurately transform a frequency from one language to another. Results showing that, as proved by BLEU system, the proposed method can substantially outperform standard Rule Based approach and EM algorithm in terms of frequency and accuracy. The results of this study have been showed that the score of HMT system is higher than SMT system in all cases. When combining two approaches, HMT outperformed SMT in Bleu score.</p>

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.

Hybrid Arabic-English Machine Translation to Solve Reordering and Ambiguity Problems

The problem in Arabic to English rule-based machine translation is that the rule-based lexical analyzer leaves some amount of ambiguity; therefore a statistical approach is used to resolve the ambiguity problem. Rule Based Machine Translation (RBMT) uses linguistic rule between two languages which is built manually by human in general, whereas SMT uses appearance statistic of word in parallel corpora. In this paper, those different approaches are combined into Arabic-English Hybrid Machine Translation (HMT) system to get the advantage from both kind of information. In the beginning, Arabic text will be inputted into RBMT to solve reordering problem. Then, the output will be edited by SMT to solve the ambiguity problem and generate the final translation of English text. SMT is capable to do this because on the training process, it uses RBMT’s output (English) as source material and real translation (English) as target material. The results showed that the quality of translation in HMT system is better than SMT system.

Projetos sobre Tradução Automática do Português no Laboratório de Sistemas de Língua Falada do INESC-ID

Language technologies, in particular machine translation applications, have the potential to help break down linguistic and cultural barriers, presenting an important contribution to the globalization and internationalization of the Portuguese language, by allowing content to be shared 'from' and 'to' this language. This article aims to present the research work developed at the Laboratory of Spoken Language Systems of INESC-ID in the field of machine translation, namely the automated speech translation, the translation of microblogs and the creation of a hybrid machine translation system. We will focus on the creation of the hybrid system, which aims at combining linguistic knowledge, in particular semantico-syntactic knowledge, with statistical knowledge, to increase the level of translation quality.

Building a Hybrid Machine Translation System for Translating from English into Persian

The incredible speed of contemporary modern world has made using machine translation/ MT an essential need. This paper aims at describing a hybrid machine translation system conducted for translation of some simple declarative English sentences into Persian. It is called a hybrid machine translation system because it employs a combination of both rule-based approaches and corpus-based approaches. The results of the research suggest that the given machine translation system produces more natural sentences which are compatible with Persian word order.

Multi-objective optimisation of real-valued parameters of a hybrid MT system using Genetic Algorithms

In this paper, an automated method is proposed for optimising the real-valued parameters of a hybrid Machine Translation (MT) system that employs pattern recognition techniques together with extensive monolingual corpora in the target language from which statistical information is extracted. The absence of a parallel corpus prohibits the use of the training techniques traditionally employed in state-of-the-art Statistical Machine Translation systems. The proposed approach for fine-tuning the system parameters towards the generation of high-quality translations is based on a Genetic Algorithm and the multi-objective evolutionary algorithm SPEA2. In order to evaluate the translation quality, established MT automatic evaluation criteria are employed, such as BLEU and METEOR. Furthermore, various ways of combining these criteria are explored, in order to exploit each one’s characteristics and evaluate the produced translations. The experimental results indicate the effectiveness of this approach, since the translation quality of the evaluation sentence sets used is substantially improved in all studied configurations, when compared to the output of the same system operating with manually-defined parameters. Out of all configurations, the multi-objective evolutionary algorithms, combining several MT evaluation metrics, are found to produce the highest quality translations.