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This paper hypothesizes that transfer-based machine translation systems can be improved by encoding information structure in both the source and target grammars, and preserving information structure in the transfer stage. We explore how information structure can be represented within the HPSG/MRS formalism (Pollard and Sag, 1994; Copestake et al., 2005) and how it can help refine multilingual MT. Building upon that framework, we provide a sample translation between English and Japanese and check the feasibility of the proposals in small-scale translation systems built with the HPSG/MRS-based LOGON MT infrastructure (Oepen et al., 2007). Our experiment shows the information structure-based MT system that we propose in this paper reduces the number of translations 75.71% for Japanese and 80.23% for Korean. The dramatic reductions in the number of translations is expected to make a contribution to our HPSG/MRS-based MT in terms of latency as well as accuracy.
Several phenomena associated with the differences in the performance of novice interpreters and semi-professionals have been discussed in the paper. Particular emphasis was placed on the occurrence of imported cognitive load which strongly influenced the performance of the subjects also in places where no intrinsic difficulty had been detected. Nevertheless, too little evidence was provided to establish a more detailed pattern of imported cognitive load, which was due to the limited number of participants in the study. It would be possible to obtain more detailed data and comments from the participants by means of interviews conducted individually with the participants. It would allow asking detailed questions to the participants, which might be a more reliable method than the immediate retrospective accounts. Moreover, in the present study such variables as gender differences, age differences and the possible influence of other foreign languages were not taken into account. Perhaps these variables might shed some light on the issue of the management of cognitive resources. Also, the corpus gathered for the present study may be used for the investigation of other aspects of the SI performance.
This article will attempt to suggest translation procedures necessary to translate culturally bound items in the referential level of a literary work illustrated with examples from two novels: “The Bluest Eye” by Toni Morrison and “Vineland” by Thomas Pynchon. First, the article will include a general description of the referential level in literary works offering possible avenues of 285 its rendition, then and finally suggest a translation methodology and techniques together with practical examples of the theory at work.
Metaphorization and selected translation techniques : the case study of "National Geographic"
(2008)
I shall use the precise term 'interlinear morphemic translation (IMT) to designate the object of this study. [...] An IMT is a translation of a text in a language L1 to a string of elements taken from L2 where, ideally, each morpheme of the L1 text is rendered by a morpheme of L2 or a configuration of symbols representing its meaning and where the sequence of the units of the translation corresponds to the sequence of the morphemes which they render. [...] An IMT is needed whenever it is essential that the reader grasp the grammatical structure of the L1 text but is presumed to be so unfamiliar with L1 that he will not be able to do so merely with the aid of a normal translation and the context in which the text is cited. [...] The primary aim of an IMT is to make the grammatical structure of the L1 text transparent. The textual fluency of the IMT by standards of the L2 grammar is a subordinate aim at best.
The aim of any Automatic Translation project is to give a mechanical procedure for finding an equivalent expression in the target language to any sentence in the source language. The aim of my linguistic translation project is to find the corresponding structures of the languages dealt with. The two main problems that have to be solved by such a project are the difference of word order between the source language and the target language and the ambiguous words of the source language for which the appropriate word in the target language has to be chosen. The first problem is of major linguistic interest: once the project has been worked out, it will give us the parallel sentence structures for the two languages in question. Since there is no complete analysis of any language that could be used for the purpose of automatic translation, we decided to build up our project sentence by sentence. The rules which are needed for translating each sentence will have to be included in the complete program anyway, and the translation may be checked and corrected immediately. The program is split up into subroutines for each word-class, so that a correction of the program in case of an unsatisfactory translation does not complicate the program unnecessarily.