Target Language Corpus Research Articles

Register variation in school EFL textbooks

Abstract This study applies additive Multi-Dimensional Analysis (MDA) (Biber 1988) to explore the linguistic characteristics of ‘school English’ or ‘textbook English’. It seeks to find out how text registers commonly featured in English as a Foreign Language (EFL) textbooks differ from comparable registers found outside the EFL classroom. To this end, a Textbook English Corpus (TEC) of 43 coursebooks used in European schools is mobilised. The texts from six textbook register subcorpora and three target language corpora are mapped onto Biber’s (1998) ‘Involved vs. Informational’ dimension of General English. Register accounts for 63% of the variance in these dimension scores in the TEC. Additional factors such as textbook level, series and country of publication/use only play a marginal role in mediating textbook register variation. Textbook dialogues score considerably lower than the Spoken BNC2014, whereas Textbook Fiction scores closest to its corresponding reference Youth Fiction Corpus. Pedagogical and methodological implications are discussed.

A CORPUS-BASED STUDY ON PREPOSITIONAL PHRASES IN LEGAL VOCABULARY IN ENGLISH AND BULGARIAN

An examination into the linguistic specificities of legal terminology and phraseology asks for an in-depth expertise of legal vocabulary and profound awareness of the distinctive features of legal matters and procedures. Since it is a strict formulaic language that is used in legal papers and documents, the majority of single-word terms, notions and concepts are literally translated into the target language, that is, word-for-word interpretation without losing or lacking any grammatical or semantic meaning of the source language. Nevertheless, there is a group of lexical items, namely, particular prepositional phrases, which require a particular research approach while being transferred to the receptor language (Alcaraz, Hughes 2002, Muller 2016). In this case the grammatical form often cannot be reproduced though the meaning and content may remain intact. Furthermore, there could be a case for saying that the source language may represent a number of prepositional phrases bearing the same propositional attitude while the target language may reveal only a limited set or no identical phrase at all (Bajcic 2017). The present article focuses on two corpora of database taken from the official legal framework concerning the European investigation order and its Bulgarian translation with regards to particular formulaic linguistic conventions, multiword prepositional phrases, in particular. As the study is both corpus-based and corpus-driven it does not only validate or refute previous theoretical research findings but also involves the inductive reasoning to draw conclusions (Iglikova 2017). Specifically, the paper dwells on six prepositional phrases in English which are a typical and integral part of any legal file though their translation equivalents may vary in grammatical meaning and deviate from the source language propositional meaning (Alexandrova 2018). They are as follows: in accordance with, pursuant to, for the purpose of, without prejudice to, subject to, according to. Additionally, the paper makes an attempt to identify their translation variants in the target language corpus considering the level of identicality, their similarities and discrepancies. The expressions are analyzed and examined quantitatively and qualitatively, the latter focusing on the syntax and semantics of the phrases.The paper initiates with a brief theoretical overview of prepositional and multiword prepositional phrases regarding their grammatical form and structure followed by an analytical part divided into two sections examining English and Bulgarian corpora separately. The same pattern of analysis is used in evaluating and interpreting both sets of data.The paper targets primarily MA students in Linguistics and Translation and aims to help learners get a closer look into prepositional phrases which do not bear a central meaning in a sentence but can turn to be a stumbling block if not translated adequately.

Phrase-boundary model for statistical machine translation

This paper proposes a new probabilistic synchronous context-free grammar model for statistical machine translation. The model labels nonterminals with classes of boundary words on the target side of aligned phrase pairs. Labeling of the rules is performed with coarse grained and fine grained nonterminals using POS tags and word clusters trained on the target language corpus. Considering the large size of the proposed model due to the diversity of nonterminals, we have also proposed a novel approach for filtered rule extraction based on the alignment pattern of phrase pairs. Using limited patterns of rules, the extraction of hierarchical rules gets restricted from phrase pairs that are decomposable to two aligned subphrases. The proposed filtered rule extraction decreases the model size and the decoding time considerably with no significant impact on the translation quality. Using BLEU as a metric in our experiments, the proposed model achieved a notable improvement rate over the state-of-the-art hierarchical phrase-based model in the translation from Persian, French and Spanish to English language. This is applicable for all languages, even under-resourced ones having no linguistic tools.

Target Language Corpus as an Encoding Tool: Collocations in Slovene-English Translator Training

The opening part of the article discusses reasons for the lukewarm reception of language corpora in the language teaching community. The first reason is the complex syntax and rudimentary user interface of early corpora accessible in the 1990s. The second reason why corpora have witnessed a relatively slow start in language teaching is the fear of the unknown and of an unruly linguistic reality that is often at odds with rules taught at school. The practical part of the article presents a survey conducted among Slovene university students of translation. The survey focused on the effect of using a target language corpus in the course of Slovene into English translation in terms of English collocation. It found that the number of collocation errors in translation can be greatly reduced by competent use of a L2 corpus, which yields a translation with a higher level of idiomaticity.

Improving Machine Translation Using Hybrid Dictionary-Graph Based Word Sense Disambiguation with Semantic and Statistical Methods

This paper describes a machine translation prototype in which noun phrase translation is defined as a subtask of machine translation. A dedicated noun phrase translation subsystem is built and improved to translate Arabic noun phrase into English using only minimal resources for both the source and the target language. This work proposes a dictionary-graph based WSD approach to improve machine translation using hybrid semantic-statistical method based on computing words relatedness and a statistical measure of association to get the relation between ambiguous words. This relation was used with viterbi search algorithm to find the appropriate translation of the Arabic noun phrase. A shallow source language analysis, combined with a translation dictionary and a mapping system of source language phenomena into the target language and a target language corpus for generation are all the resources needed in the described system.

Translating metadiscourse in research articles

Translating research articles is common practice, although relatively little data exists on the problems in this type of translation. This study examines the translation of textual metadiscourse in academic writing, using the example of translating Slovene research articles into English. The main part of the analysis examines textual metadiscourse in 30 geography articles in Slovene and their translations into English. In addition, the same investigation is conducted in a corpus of 30 English-original geography articles. The translation strategies are analysed at two levels. At the first level, the overall strategy is analysed: the metadiscourse items are compared in the originals and the translations and instances of matching expressions, omissions, and insertions are identified. At the second level, the corresponding expressions are compared and changes made in the translation are noted. The overall results are then compared to those of the comparable target language subcorpus. The results show that not all metadiscourse items found in the original texts are translated, while, at the same time, a significant number of items are inserted in the translation. For those metadiscourse items which are translated, literal translation is chosen in over half of the cases. The results for the comparable target language corpus reveal that metadiscourse is used more frequently in English originals than in translations from Slovene.

Utilizing Clues in Syntactic Relationship for Automatic Target Word Sense Disambiguation

Multiple translations to the target language are due to several meanings of source words and various target word equivalents, depending on the context of the source word. Thus, an automated approach is presented for resolving target-word selection, based on word-to-sense and sense-to-word relationship between source words and their translations, using syntactic relationships (subject-verb, verb-object, adjective-noun). Translation selection proceeds from sense disambiguation of source words based on knowledge from a bilingual dictionary and word similarity measures from WordNet, and selection of a target word using statistics from a target language corpus. Test results using English to Tagalog translations show an overall 64% accuracy for selecting word translation with a standardized precision of at least 80% for generating expected translations using 200 sentences with ambiguous words (an average of 4 senses) in three categories: nouns, verbs, and adjectives. This system is tested on 145,746 word pairs in syntactic relationships that are extracted from target corpora (with 317,113 words). Sense profile, with 2681 entries for source words, is built from an existing bilingual dictionary that includes clues for disambiguation and target translations. The results show an improvement on the performance of the method with the utilization of syntactic information in resolving target-word ambiguity. Further improvements include the integration of other content words and syntactic categories, the addition of reliable clues for sense disambiguation, and the integration of smoothing techniques.

METIS-II: low resource machine translation

METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their home languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.

A Study on Cross Transformation of Mongolian Language

This paper discusses a segmentation approach of Mongolian for Cyrillic text for machine translation. Using this method, the processing of one-to-one word permutation between the variations of Mongolian and other languages, especially Altaic family languages like Japanese, becomes easier. Furthermore, it can be used for two-way conversion between texts of Mongolian used in different regions and counties, such as Mongolia and China. Our system has been implemented based on DP (dynamic programming) matching supported by knowledge-based sequence matching, referred to as a multilingual dictionary and linguistic rule bank (LRB), and a data-driven approach of the target language corpus (TLC). For convenience, NM (New Mongolian) is treated as the source language, and TM (Traditional Mongolian) and Todo as the target language in this test. Our application was tested using manually transcribed texts with sizes of 5, 000 sentences paralleled from NM to TM and Todo. We found that our method could achieve 91.9% of the transformation accuracy for “NM” to “TM” and 94.3% for “NM” to “Todo”.

‘Irrefragable answers’ using comparable corpora to retrieve translation equivalents

In this paper we present a tool that uses comparable corpora to find appropriate translation equivalents for expressions that are considered by translators as difficult. For a phrase in the source language the tool identifies a range of possible expressions used in similar contexts in target language corpora and presents them to the translator as a list of suggestions. In the paper we discuss the method and present results of human evaluation of the performance of the tool, which highlight its usefulness when dictionary solutions are lacking.

Indonesian-Japanese Transitive Translation using English for CLIR

We propose a query transitive translation system of a CLIR (Cross Language Information Retrieval) for a source language with a poor data resource. Our research aim is to do the transitive translation with a minimum data resource of the source language (Indonesian) and exploit the data resource of the target language (Japanese). We did two kinds of translation, a pure transitive translation and a combination of direct and transitive translations. In the transitive translation, English is used as the pivot language. The translation consists of two main steps. The first is a keyword translation process which attempts to make a translation based on available resources. The keyword translation process involves many target language resources such as the Japanese proper name dictionary and English-Japanese (pivot-target language) bilingual dictionary. The second step is a process to select some of the best available translations. We combined the mutual information score (computed from target language corpus) and TF × IDF score in order to select the best translation. The result on NTCIR 3 (NII-NACSIS Test Collection for IR Systems) Web Retrieval Task showed that the translation method achieved a higher IR score than the machine translation (using Kataku (Indonesian-English) and Babelfish/Excite (English-Japanese) engines). The transitive translation achieved about 38% of the monolingual retrieval, and the combination of direct and transitive translation achieved about 49% of the monolingual retrieval which is comparable to the English-Japanese IR task.

Applied linguistics

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Translation Selection by Combining Multiple Measures for Sense Disambiguation and Word Selection

Translation selection is a process to select, from a set of target language words corresponding to a source language word, the most appropriate one that conveys the correct sense of a source word and makes the target language sentence more natural. In this paper, we propose a hybrid method for translation selection that exploits a bilingual dictionary and a target language corpus. Based on the 'word-to-sense and sense-to-word' relationship between a source word and its translations, our method selects translation through two levels: sense disambiguation of a source word and selection of a target word. For translation selection, we introduce three measures: sense preference and sense probability for sense disambiguation, and word probability for word selection. The first one is based on knowledge from a bilingual dictionary, and the others are calculated using statistics from a target language corpus. We evaluated our method and results showed that our method selects more appropriate target words with knowledge extracted from easily obtainable resources.

On the Relative Influence of Corpus and Dictionary Size in a Study Using Non-Parallel Corpora

We did an experiment on Japanese-to-German translation of 2-part compound nouns via their components using a small dictionary and a large Target Language (TL) corpus. As TL translation variants, we considered expressions containing adjectives or genitive adjuncts, as well as diverse forms for the first component of a German compound. Verification in a TL corpus is a good means of deciding among these forms, at least. In order to get significant statistics from corpora, large data quantities are important. As parallel data are still quite scarce, using monolingual corpora instead is an option, but it requires the use of a dictionary. In our study, insufficient dictionary size was an obstacle much bigger than corpus size. We tried to quantify the relative influence of the two resources to assess system balance. We predict that a middle-sized dictionary of about 100,000 entries would give good coverage of compound noun components.

Towards a Methodology for Exploiting Specialized Target Language Corpora as Translation Resources

Specialized target language (TL) corpora constitute an extremely valuable resource for translators, and although no specialized tools have been developed for extracting translation data from such corpora, this paper argues that translators would be remiss not to consult such resources. We describe the advantages of using specialized TL corpora and outline a number of techniques that translators can use in order to extract translation data from such corpora with the aid of generic corpus analysis tools. These advantages and techniques are demonstrated with reference to two translations, one of which was done using only conventional resources and the other with the help of a corpus.