This kind of stochastic tool will generally be able to present the user with numerous translation predictions of various lengths. 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.
![transtype open source transtype open source](https://community.dynamics.com/cfs-file/__key/communityserver-discussions-components-files/33/rangeFilter.jpg)
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
![transtype open source transtype open source](https://www.bettertechtips.com/wp-content/uploads/2017/11/open-source-erp.jpg)
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.
![transtype open source transtype open source](https://www.sketchpad.net/images/transtypem11.gif)
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.