Target Language Sentences Research Articles

Structure Shift in The Indonesian Translation of Pragmatics Textbook Written by George Yule

This study analyzed the structure shift that occurred in English-Indonesian translation. This study employed the descriptive qualitative approach. Data that were found in this research were sentences in the Indonesian translation version of Pragmatics written by George Yule. The textbooks were the source of the data, one as a source language which is English version, another as a target language in Indonesian version. The textbook entitled Pragmatics by George Yule was as a source language, and Its translation which entitle Pragmatik as a target language. The data were collected using document analysis, and data sheet that used to categorize the types of structure shift. There are 70 structure shifts found from the data that were collected and analyzed. There are three types of structure shift. The structure shift of addition appeared 13 times, Omission appeared 21 times, and the structure shift of Head Modifier to Modifier Head in phrase of the sentences in source language and target language appeared 36 times.

Informācijas izgūšanas nozīme un daži ar sintaksi saistīti apsvērumi juridisko tekstu tulkošanā

The translation of legal documents – not a new field in translation practice or theoretical discourse – gained a new dimension for translators’ work in Latvia when, after restoring independence, the country was reintegrated into international processes and organizations. Consequently, the development of legal text translation competence has also become an important task in the study programs related to translation of LSP texts. Against this background, the paper addresses some of the issues of understanding and interpreting legislation in the translation situation, with a particular focus on working with the functions and implications of sentence syntax. This part of the work provides the translator with the opportunity to find not only successful grammatical solutions in the target language sentences, but above all, a prerequisite for understanding the meaning of the source text. For the purposes of the study, the relevant aspects are briefly outlined in a theoretical context by focusing on the specific features of legal texts and the competence-related requirements for translators; it also includes an analysis of examples based on both published translations of legislation and the typical problems encountered in student translations. The study leads to several conclusions. Accuracy (also with regard to interpretation), an element of the general concepts of equivalence/adequacy, stands out as a specific aspect and criterion of legal text translation quality; it is necessary to ensure that the meaning of terms is not broadened or narrowed and that the applicability or explicit/implicit attitude is not altered – translations of a number of units and elements tend to be almost literal. The practice of translating legal texts generally requires that target texts be rendered as consistently as possible, which to a large extent implies an almost literal relationship with the source text; any changes need explicit justification. A specific aspect of translators’ competence is the examination undertaken during the pre-translation phase to determine the applicability of the relevant legal provisions and select the most appropriate sources of information. An important prerequisite for a quality translation is understanding the essence of the source sentence.

A Sanskrit-to-English machine translation using hybridization of direct and rule-based approach

The work in this paper presents a MTS from Sanskrit to English language using a hybridized form of direct and rule-based machine translation technique. This paper also discusses the language divergence among Sanskrit and English languages with a recommended solution to handle the divergence. The proposed system has used two bilingual dictionaries (Sanskrit–English, Sanskrit–UNL), a tagged Sanskrit corpus, a Sanskrit analysis rule base and an ELGR base. Elasticsearch technique has enhanced the translation speed of the proposed system for accessing the data from different data dictionaries and rule bases used for the system development. The system uses CFG in CNF for Sanskrit language processing and CYK parsing technique for processing the input Sanskrit sentence. This work also presents a novel algorithm which creates a parse tree from the parsing table. ELGR base and bilingual dictionaries generate the target language sentence. The proposed system is evaluated using natural language toolkit API in python and achieved a BLEU score of 0.7606, fluency score of 3.63 and adequacy score of 3.72. A comparison of the proposed system with state-of-the-art systems shows that the proposed system outperforms existing systems.

PERUBAHAN BENTUK KALIMAT PASIF BAHASA INGGRIS KE DALAM BAHASA INDONESIA PADA NOVEL MORNING, NOON AND NIGHT KARYA SIDNEY SHELDON

This paper focused on English passive voice translation into Indonesian. An active voice is a sentence where the subject performs the action stated by the verb, and a passive voice is the subject is acted upon by the verb. The active voice that can be switched into a passive voice is an active voice that has an object. Thus, the sentence is a transitive form. The method used is a qualitative method by using a technique of text content analysis and the text are English novel and its translation in Indonesian novel. This research is an analysis description on the novel that included passive voice translation and structural shift. This paper explained the results of the study of the passive sentences of the source language (English) translated into Indonesian. The results of the study show that not all of the passive sentences of the source language can be translated in the form of passive sentences in target language. Instead, the passive sentences can be translated in the form of active sentences because of the meaning contained.

An Automatic and a Machine-assisted Method to Clean Bilingual Corpus

Two different methods of corpus cleaning are presented in this article. One is a machine-assisted technique, which is good to clean small-sized parallel corpus, and the other is an automatic method, which is suitable for cleaning large-sized parallel corpus. A baseline SMT (MOSES) system is used to evaluate these methods. The machine-assisted technique used two features: word alignment and length of the source and target language sentence. These features are used to detect mistranslations in the corpus, which are then handled by a human translator. Experiments of this method are conducted on the English-to-Indian Language Machine Translation (EILMT) corpus (English-Hindi). The Bilingual Evaluation Understudy (BLEU) score is improved by 0.47% for the clean corpus. Automatic method of corpus cleaning uses a combination of two features. One feature is length of source and target language sentence and the second feature is Viterbi alignment score generated by Hidden Markov Model for each sentence pair. Two different threshold values are used for these two features. These values are decided by using a small-sized manually annotated parallel corpus of 206 sentence pairs. Experiments of this method are conducted on the HindEnCorp corpus, released in the workshop of the Association of Computational Linguistics (ACL 2014). The BLEU score is improved by 0.6% on clean corpus. A comparison of the two methods is also presented on EILMT corpus.

Research on the Information Processing Model of Source Language of Parallel Corpus

The rapid development of corpus-based machine translation brings opportunities and challenges to the construction of corpus. With the continuous development of information technology, the information processing model of the source language has always been an academic hot topic in natural language processing. In the current machine translation framework, this paper proposes a parallel corpora source language processing model for translation equivalent trees. Firstly, the phrases in the source language and the target language sentence are tagged with parts of speech respectively; Secondly, the word alignment tool is used to align the phrase pairs of the source language and the target language; Syntax analysis is used to parse the source language sentence; Then, a translation equivalence tree is constructed based on the source language syntax tree, target language sentence and word alignment results. Finally, phrase translation pairs are extracted from the translation equivalent tree.

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.

An Overview of Statistical Machine Translation Tools

The process Machine translation is a combination of many complex sub-processes and the quality of results of each sub-process executed in a well defined sequence determine the overall accuracy of the translation. Statistical Machine Translation approach considers each sentence in target language as a possible translation of any source language sentence. The possibility is calculated by probability and as obvious, sentence with highest probability is treated as the best translation. SMT is the most favoured approach not only because of its good results for corpus rich language pairs, but also for the tools that SMT approach has been enhanced with in past two and half decades. The paper gives a brief introduction to SMT: its steps and different tools available for each step.

Preordering using a Target-Language Parser via Cross-Language Syntactic Projection for Statistical Machine Translation

When translating between languages with widely different word orders, word reordering can present a major challenge. Although some word reordering methods do not employ source-language syntactic structures, such structures are inherently useful for word reordering. However, high-quality syntactic parsers are not available for many languages. We propose a preordering method using a target-language syntactic parser to process source-language syntactic structures without a source-language syntactic parser. To train our preordering model based on ITG, we produced syntactic constituent structures for source-language training sentences by (1) parsing target-language training sentences, (2) projecting constituent structures of the target-language sentences to the corresponding source-language sentences, (3) selecting parallel sentences with highly synchronized parallel structures, (4) producing probabilistic models for parsing using the projected partial structures and the Pitman-Yor process, and (5) parsing to produce full binary syntactic structures maximally synchronized with the corresponding target-language syntactic structures, using the constraints of the projected partial structures and the probabilistic models. Our ITG-based preordering model is trained using the produced binary syntactic structures and word alignments. The proposed method facilitates the learning of ITG by producing highly synchronized parallel syntactic structures based on cross-language syntactic projection and sentence selection. The preordering model jointly parses input sentences and identifies their reordered structures. Experiments with Japanese--English and Chinese--English patent translation indicate that our method outperforms existing methods, including string-to-tree syntax-based SMT, a preordering method that does not require a parser, and a preordering method that uses a source-language dependency parser.

A Comparative Analysis of Elementary English Classroom Characteristics in Korea and Japan

The aim of this study is to investigate and compare the present elementary school English education circumstances in Korea and Japan in terms of the similarities and differences of the pedagogical practices as well as of the teachers’ pedagogical views on teaching English to young learners. Questionnaires, observations, and interviews were employed in order to gather the quantitative and qualitative data. The five major findings indicate the following: a) practicing CLT in both countries was similar, but in Japan, JLTs (Japanese assistant language teacher) helped the main teacher and students; b) the use of materials and activities was similar, but Korean teachers preferred CD-ROMs, whereas they were not popular with Japanese teachers; c) the majority of Korean and Japanese teachers did not favor exclusive L2 use; d) while Korean teachers highlighted the key target language sentences in the initial phase of class, Japanese teachers did not require students to be aware of them; e) teachers in both countries had a different attitude in generating student motivation. In the final section, this study has attempted to provide implications for the future education of both countries.

Cross-Lingual Similarity Discrimination with Translation Characteristics

In cross-lingual plagiarism detection, the similarity between sentences is the basis of judgment. This paper proposes a discriminative model trained on bilingual corpus to divide a set of sentences in target language into two classes according their similarities to a given sentence in source language. Positive outputs of the discriminative model are then ranked according to the similarity probabilities. The translation candidates of the given sentence are finally selected from the top-n positive results. One of the problems in model building is the extremely imbalanced training data, in which positive samples are the translations of the target sentences, while negative samples or the non-translations are numerous or unknown. We train models on four kinds of sampling sets with same translation characteristics and compare their performances. Experiments on the open dataset of 1500 pairs of English Chinese sentences are evaluated by three metrics with satisfying performances, much higher than the baseline system.

Analisis Penerjemahan Kalimat Pasif Bahasa Inggris Ke dalam Bahasa Indonesia pada Novel Morning, Noon and Night Karya Sidney Sheldon

A translated novel can enrich the culture and life of other nations. In order to properly understand the intended meaning of the author, the translator must understand both the source language and the target language. Meaning is an important element in the translation. Article explained the results of the study of the passive sentences of the source language (English) translated into Indonesian. The results of the study show that not all of the passive sentences of the source language can be translated in the form of passive sentences in target language. Instead, the passive sentences can be translated in the form of active sentences because of the meaning contained.

Advanced Cantonese ESL Learners' Use of a Monolingual Dictionary for Language Production

This article reports on the results of a research study which investigated the use of monolingual dictionaries by Hong Kong advanced Cantonese ESL learners in the production of target language sentences. Thirty-one English majors participated in a sentence completion task and a sentence construction task with and without the help of a monolingual dictionary. In the sentence completion task, a full Chinese context and a partial English context were given, whereas in the sentence construction task, only a few English prompts were given. Different self-reporting protocols, including introspective questionnaires, retrospective questionnaires and think-aloud recordings, and a post-task focus-group interview were conducted to tap into the participants' thinking processes during dictionary consultation. The results show that a monolingual dictionary is useful in helping learners produce target language sentences, yet learners encounter different kinds of consultation problems, some of which are related to their general use of dictionaries and others to the language in which their thinking processes are engaged. It is suggested that ESL learners use both monolingual and bilingualized dictionaries in their learning and that ESL teachers design dictionary skills training programs which take into account learners' linguistic competence and actual consultation problems.

Advanced Cantonese ESL Learners' Use of a Monolingual Dictionary for Language Production

This article reports on the results of a research study which investigated the use of monolingual dictionaries by Hong Kong advanced Cantonese ESL learners in the production of target language sentences. Thirty-one English majors participated in a sentence completion task and a sentence construction task with and without the help of a monolingual dictionary. In the sentence completion task, a full Chinese context and a partial English context were given, whereas in the sentence construction task, only a few English prompts were given. Different self-reporting protocols, including introspective questionnaires, retrospective questionnaires and think-aloud recordings, and a post-task focus-group interview were conducted to tap into the participants' thinking processes during dictionary consultation. The results show that a monolingual dictionary is useful in helping learners produce target language sentences, yet learners encounter different kinds of consultation problems, some of which are related to their general use of dictionaries and others to the language in which their thinking processes are engaged. It is suggested that ESL learners use both monolingual and bilingualized dictionaries in their learning and that ESL teachers design dictionary skills training programs which take into account learners' linguistic competence and actual consultation problems. Keywords: Lexicography, Monolingual Dictionaries, Dictionary Use, Dictionary Consultation, Usefulness Of Dictionaries, Language Production, Sentence Completion, Sentence Construction, Cantonese Esl Learners, Problems Encountered, Contexts Given

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

The development of machine translation (MT) system for ancient language such as Sanskrit is a much more fascinating and challenging task. Due to lack of linguistic community, there are no wide works accomplished in Sanskrit translation while it is national language by virtue of its importance in India's cultural heritage. This paper describes a rule-based approach of machine translation which translates an English sentence (source language sentence) into equivalent Sanskrit sentence (target language sentence). Due to morphological richness of Sanskrit language, this system makes limited use of syntax and uses only morphological markings to identify subject, object, verb, preposition, adjective and adverb, as well as conjunctive sentences. It uses limited parsing for part of speech (POS) tagging, identification of clause, its subject, object, verb, etc and gender-number-person (GNP) of noun and object. This system gives translation result in GUI form and handles English sentences of different classes. The performance evaluations of our EST system with different methods of MT evaluations are shown using table and column chart.

The Application of Chomsky’s Syntactic Theory in Translation Study

Noam Chomsky is a famous American linguist. Since he put forward his Transformational- generative Grammar (TG), many scholars have tried to use the concepts of deep structure (D-structure) and surface structure (S-structure) to provide a theoretical basis for translation studies. It is true that the theory of D- / S-structure is of great importance in analyzing the syntactic structure of sentences in source language and target language. However, in the author’s opinion, the function of D- / S-structure cannot be exaggerated in translation study as translation is not just a syntactic process but a combination of many aspects.

Discriminative Word Alignment by Linear Modeling

Word alignment plays an important role in many NLP tasks as it indicates the correspondence between words in a parallel text. Although widely used to align large bilingual corpora, generative models are hard to extend to incorporate arbitrary useful linguistic information. This article presents a discriminative framework for word alignment based on a linear model. Within this framework, all knowledge sources are treated as feature functions, which depend on a source language sentence, a target language sentence, and the alignment between them. We describe a number of features that could produce symmetric alignments. Our model is easy to extend and can be optimized with respect to evaluation metrics directly. The model achieves state-of-the-art alignment quality on three word alignment shared tasks for five language pairs with varying divergence and richness of resources. We further show that our approach improves translation performance for various statistical machine translation systems.

A Cross-Language State Sharing and Mapping Approach to Bilingual (Mandarin–English) TTS

We propose a hidden Markov model (HMM)-based bilingual (Mandarin and English) text-to-speech (TTS) system to synthesize natural speech for given bilingual text. A simple baseline system consisting of two independent monolingual HMM synthesizers is built first from corresponding Mandarin and English data recorded by a bilingual speaker. A new, mixed language TTS is then constructed by asking language-independent and language-specific questions for sharing HMM states across the two languages in decision-tree based clustering. By sharing states, the new system has a smaller footprint than the baseline system. Speech synthesized by the new system sounds very similar to the baseline for non-mixed, Mandarin or English, monolingual sentences but much better for mixed-language sentences. This higher quality of mixed-language output is confirmed by a preference score, 60.2% to 39.8%, in a subjective listening test. A cross-language state mapping algorithm is further proposed for cross-language synthesis when only monolingual (English) recorded data from a source language speaker is available. Mandarin speech is then synthesized with the HMM model parameters in the nearest neighbor leaf nodes of the English decision tree. The nearest neighbor is measured with the Kullback-Leibler divergence (KLD) and mappings between leaf nodes in the decision trees of the source and target languages are established via the speech data recorded by a different, bilingual speaker. High voice (speaker) similarity is preserved in the synthesized target language sentences by using the recording of a source language from a monolingual speaker. Perceptual test results conducted on synthesized Mandarin speech show 1) high intelligibility which is confirmed by a Chinese character transcription accuracy of 92.1% and 2) decent speech quality with an average MOS score of 3.1.

Language transference rule producing apparatus, language transferring apparatus method, and program recording medium

A language transferring apparatus is provided. A language rule producing section statistically automatically trains grammatical or semantic restriction rules for a partial word or a word string from a parallel-translation corpus. The rules are described such that a source language partial sentence corresponds to a target language partial sentence. A speech recognizing section performs speech recognition on source language speech using the language rules and outputs a recognition result. A language transferring section transfers a source language sentence into a target language sentence using the same language rules. Even when a portion of an input speech sentence contains an untrained portion or when speech recognition is partly erroneously performed, transference to the target language is surely enabled. Moreover, a phrase dictionary and interphrase rules which are necessary for transference can be automatically produced without requiring much manual assistance.