Translation Techniques of Twin Formulas Proverbs Into Indonesian

This study was aimed at describing the unique characteristics of twin formulas and analyzing translation techniques applied. The meaning equivalence within the twin formulas was analyzed based on denotative and connotative approach. Library research and qualitative method were used in this study. The proverbs being analyzed were taken from a bilingual self-help book, entitled “Awaken the Giant Within” written by Robbins (1991) and its translation Bangunkan Kuasa Raksasa di Dalam Diri translated by Saputra (2006). The theory about proverb types proposed by Mieder (2004) was used as the main theory. Translation theory proposed by Vinay and Dalbernet (2002) were used to analyze the translation techniques. In relation to denotative and connotative meaning of proverbs, meaning equivalence theory proposed by Leech (1974) was used to support the analysis. The collected data showed that there were 15 of 113 data were recognized having the characteristics of twin formulas. Alliterative and opposite patterns were found as variation of twin formulas. The literal translation was the translation technique mostly used. Besides, transposition, modulation and borrowing techniques were employed. Related to the meaning of proverbs, most forms were transferred denotatively

Techniques of Translating Thesis Abstracts of Economics Department Students in Medan State University

The study deals with the techniques of translation on thesis abstracts inEconomics Department. The objectives of study were to identify the types oftranslation techniques, to find out the most dominant type of translationtechniques used, and to describe the reasons of the translation techniques used intranslating thesis abstract. The study used descriptive qualitative design.Nazir(1998: 34) states that descriptive qualitative is a method of research thatmakes the description of the situation of events or occurrences clearer. It isunderstood that descriptive qualitative is a method of research which provides thedescription of situation, events or occurrences, so this method is an intention toaccumulate the basic data. Qualitative research involves analysis of data such aswords and phrases written in abstracts. The data were taken from twentytranslated thesis abstracts of Economic Department. The findings show that therewere eight techniques of eighteen techniques used in thesis abstracts. The mostdominant type of translation techniques was established equivalent due to thetranslator intention to avoid misunderstanding by using the dictionaries andparticular equivalent known by target language. It is recommended that in doingany translation, the most essential thing is to keep the meaning or the message ofthe source language remains the same when it is being translated into the targetlanguage

Multilingual search for cultural heritage archives via combining multiple translation resources

The linguistic features of material in Cultural Heritage (CH) archives may be in various languages requiring a facility for effective multilingual search. The specialised language often associated with CH content introduces problems for automatic translation to support search applications. The MultiMatch project is focused on enabling users to interact with CH content across different media types and languages. We present results from a MultiMatch study exploring various translation techniques for the CH domain. Our experiments examine translation techniques for the English language CLEF 2006 Cross-Language Speech Retrieval (CL-SR) task using Spanish, French and German queries. Results compare effectiveness of our query translation against a monolingual baseline and show improvement when combining a domain-specific translation lexicon with a standard machine translation system

Hand in hand: automatic sign Language to English translation

In this paper, we describe the first data-driven automatic sign-language-to- speech translation system. While both sign language (SL) recognition and translation techniques exist, both use an intermediate notation system not directly intelligible for untrained users. We combine a SL recognizing framework with a state-of-the-art phrase-based machine translation (MT) system, using corpora of both American Sign Language and Irish Sign Language data. In a set of experiments we show the overall results and also illustrate the importance of including a vision-based knowledge source in the development of a complete SL translation system

Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions

We present a comparative evaluation of various techniques for action recognition while keeping as many variables as possible controlled. We employ two categories of Riemannian manifolds: symmetric positive definite matrices and linear subspaces. For both categories we use their corresponding nearest neighbour classifiers, kernels, and recent kernelised sparse representations. We compare against traditional action recognition techniques based on Gaussian mixture models and Fisher vectors (FVs). We evaluate these action recognition techniques under ideal conditions, as well as their sensitivity in more challenging conditions (variations in scale and translation). Despite recent advancements for handling manifolds, manifold based techniques obtain the lowest performance and their kernel representations are more unstable in the presence of challenging conditions. The FV approach obtains the highest accuracy under ideal conditions. Moreover, FV best deals with moderate scale and translation changes

Experiments on domain adaptation for English-Hindi SMT

Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for English—Hindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline