#Transtype documentation professional

In this paper, we provide an effective method for each problem within our user interaction environment. We analyzed the problem of our system and found 3 main reasons, that is, the errors in the source sentence itself, the errors of our MT system, and the absence of the appropriate domain-specific expression information. Our original Korean-English paper MT system is quite useful for understanding, but not for writing. This paper addresses an effective method to write an English paper suitable for international conferences using our Korean-English paper MT system supported by efficient user interaction environment. Experiments are performed on the Spanish-English and German-English Xerox corpora, which consist of the translation of technical manuals, and the results show that the real time generation strategy causes only a small performance degradation. In this paper, we review possible generation strategies for an interactive statistical machine translation system and analyze what is the loss in performance when strict time constraints have to be met. In order to remain within the tight response time constraints of such a system, some simplifications have to be done. This leads to a modification of the generation strategy of the translation system, as now we are looking for the best translation of the given source sentence that is compatible with the prefix. This is the goal of the TransType2 project, where an interactive translation tool is being developed that suggests, in real time, possible completions for the sentences that the human translator is typing. Nevertheless, automatic translation systems can be used to help human translators to produce high-quality translations. Non-grammatical sentences are often produced by automatic systems and there is even no guarantee that the meaning of the sentence is preserved. We also examine differences between PE and ITP in other translation productivity indicators and translators’ reactions to the technology.įully automatic translations are far from being perfect.

#Transtype documentation professional

Our results show that over half of the professional translators in our study translated faster with NITP compared to PE, and most preferred it over PE. In this paper we present the results of an empirical study on translation productivity in ITP with an underlying neural MT system (NITP). So far, phrase-based statistical ITP has not yet proven to be faster than PE (Koehn 2009 Sanchis-Trilles et al. Both Macklovitch (2006) and Koehn (2009) found ITP to be an efficient alternative to unassisted translation in terms of processing time.

Another is interactive translation prediction (ITP), which involves an MT system presenting a translator with translation suggestions they can accept or reject, actions the MT system then uses to present them with new, corrected suggestions. A typical intervention is post-editing (PE), where a human translator corrects errors in the MT output.

Machine translation (MT) on its own is generally not good enough to produce high-quality translations, so it is common to have humans intervening in the translation process to improve MT output. Most later work on IMT, eg (Blanchon, 1991 Brown and Nirenburg, 1990 Maruyama and Watanabe, 1990 Whitelock et al., 1986), has followed in this vein, concentrating on improving the question /answer process by. IMT first appeared as part of Kay's MIND system (Kay, 1973), where the user's role was to help the computer analyze the source text by answering questions about word sense, ellipsis, phrasal attachments, etc. 1 Introduction TransType is a project set up to explore an appealing solution to the problem of using Interactive Machine Translation (IMT) as a tool for professional or other highly-skilled translators. This innovative Embedded Machine Translation system is thus a specialized means of helping produce high quality translations. This paper describes the embedding of a statistical translation system within a text editor to produce TransType, a system that watches over the user as he or she types a translation and repeatedly suggests completions for the text already entered.