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Finally, some experiments have been done with rescoring techniques based on a machine learning approach ( publications). ( publications) explored various word-based and, more recently, some phrase-based and translation memory approaches to modeling, and different decoding strategies. Various strategies to tackle this problem have focussed on improving the statistical translation models and decoding techniques.Īn innovative Maximum Entropy approach for fast text prediction of sentence fragments was proposed by Foster et al. There is often a trade-off to be made between accuracy and prediction speed. The more accurate the predictions are, the better the chances the user will accept them and save typing time. Accuracy and speed of the translation predictions. The RALI's past and present work within this project have revolved around all of these axes. In order for such a tool to actually speed up the translation process and not hinder it, there are several important factors that come into play. The Technology: realizations and challenges

Ministère du Développement économique et régional et Recherche du Québec.

International Collaborative Research and Development (CRD) grant from NSERC-IRAP.

TT2 involved international and Canadian partners.Īt the RALI, the principal researchers who were involved in the project are: Guy Lapalme, Philippe Langlais, Elliott Macklovitch, Simona Gandrabur and Ngoc Tran Nguyen. For more on the second phase of the project the TT2 flyer (PDF 100K). Two new languages (Spanish and German) and several technological enhancements were added to the initial prototype in order to improve its prediction capabilities and usability. The first phase of the project (1997-2000) was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC).Ī second phase was launched in March 2002 and ended during the summer of 2005.

You can see an animation (animated GIF) of an excerpt of a French to English translation session.

Finally, it must display its proposals clearly but unobtrusively, and provide an efficient mechanism by which they can be accepted. Second, the system must be able to operate in real time and be fast enough to keep up with its user. The tool should limit its proposals to those in which it has reasonable confidence and it should adapt its behavior to the current context, based on what it has learned from the translator. First, unlike standard MT, the system must take into account not only the source text but that part of the target text which the user has already keyed in.

This idea poses a number of technical challenges. We feel that using the target text as the medium of interaction will result in a tool that is more natural and useful for translators. While this is useful in some situations (for example, when the user has little knowledge of the target language), it has not been widely accepted by translators, in part because it requires capabilities (in formal linguistics) that are not necessarily those of a translator. In previous attempts at interactive MT, the user has had to help the system analyze the source text in order to improve the quality of the resulting machine translation. The novelty of this project lies in the mode of interaction between the user and the machine translation (MT) technology on which the system is based. when the translator makes a mistake, the system can provide a warning, either implicitly by failing to complete the mistake or explicitly by attaching an annotation for later review.when the translator is searching for an adequate formulation, the proposals can serve as suggestions.when the translator has a particular text in mind, the system's proposals can speed up the process of typing it.

#TRANSTYPE D FREE#

In the interests of helping others, we've set about collecting this information and grouping it altogether to provide an easy, simple and free resource to assist you, the community of SAP users, support technicians, consultants and developers, with a better understanding of SAP's flagship products.There are several ways in which such a system can be expected to help a translator:

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