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Integration of Machine Translation in CAT Tools: State of the Art, Evaluation and as most recent CAT software releases claim to employ advanced MT technologies. . The research conducted by (Federico et al., ) compares translators'. Designed for integration with intelligent transportation systems (ITS), ActiveVision Machine learning tool autonomously detects weather, traffic anomalies in real-time Southwest Research Institute (SwRI) has announced the release of software in addition to other ITS and advanced traffic management. Mechanical Systems --research is needed on machine dynamics (including A major gap currently exists in our ability to integrate CAD/CAM/CAT so that it is possible Interdisciplinary Research Areas Integrated System Design--advanced.


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Advanced Integration Research machine.cat Driver

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Similarly, these phrases are chosen based Advanced Integration Research machine.cat the prefix already typed by the user. The suggestions are normally short phrases, which reflects the underlying phrase-based SMT method. In addition to the autocomplete suggestions, the user can also see the most probable suggestions from the phrase table for the whole sentence.

And finally, the system also offers a post-editing function, when the users are given an entire automatically translated sentence and have to correct it. The system was evaluated in terms of translation speed and translation quality.

The fastest translation was achieved with post-editing, although autocompletion also increased the translation speed. The best quality was observed with post- editing and with the setting that includes autocompletion and phrase-table Advanced Integration Research machine.cat. These results were also complemented by a questionnaire for the participants in order to identify their opinion on the features.

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The combination of autocompletion together with the phrase- table options was reported to be the most enjoyable Advanced Integration Research machine.cat helpful for the users and also produced the most accurate translations. It has to be stressed that this work is one of the few that not only compares results with and without MT functionality, but also compares different types of MT integration post-editing and autocompletion.

The MT-based component was compared with the whole-language autocompletion, which makes suggestions based on the whole language vocabulary. The average time per segment was almost 10 seconds less with the MT- backed suggestions, which is a very significant result from the Advanced Integration Research machine.cat of view of time saving. However, the quality of translations was not evaluated. Furthermore, it would be interesting also to see how this feature works together with other common CAT tools functionalities like TM suggestions.

This type of systems can be implemented both in the IMT scenario and in the post-editing scenario.

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The incremental learning algorithm proved to reduce user effort compared to the conventional interactive MT. There are two possible ways of adaptation: The online adaptation, on the other hand, occurs almost instantly and helps to avoid the cases when the user has to correct the same MT errors multiple times Advanced Integration Research machine.cat one document.

First, they are largely divided into internal and external integration. On the other hand, external integration provides one more source of suggestions apart from the TM matches and other sources like term bases and glossaries. And finally, in real-time processing, Advanced Integration Research machine.cat suggestions can be presented to the user in form of autocompletion or interactive MT, or as additional suggestions for each segment together with the suggestions from TM and other sources.

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This scenario, in case when the MT suggestion is accepted by the user, is also referred to as the post-editing PE scenario: The key difference between the autocompletion method and the post- editing Advanced Integration Research machine.cat is the user interaction part: The survey was built online and the link to it was distributed through translation companies, mailing lists and social media groups for translators, translation blogs and translation associations.

We received completed responses and responses in total. This indicates a high Advanced Integration Research machine.cat rate but a low completion rate, which is mainly due to the large size of the questionnaire.

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The participants responded actively and many provided feedback and comments. The Advanced Integration Research machine.cat majority of translators worked as freelancers. The two largest subgroups were freelancers who had an agency but also worked independently apart, and freelancers who only worked independently. The questionnaire data was analysed in three steps.

The first step included descriptive analysis of the quantitative data, i. These results are summarised in Zaretskaya et al. Secondly, we analysed the qualitative data obtained from the open- ended questions i.

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And finally, we performed bivariate analysis to find dependencies between pairs of questions Lee and Forthofer, This makes us assume that there are a number of users who are not aware of MT integration in their software, which Advanced Integration Research machine.cat, consequently, one of the factors preventing them from adopting this technology in the most convenient and useful way. Even though MT integration in CAT is becoming more and more popular in commercial tools, and there is a considerable amount of research on the subject, it is still not clear for Advanced Integration Research machine.cat users Advanced Integration Research machine.cat it works.

Raising awareness of such technology and its potential benefits can be a way of improving the user attitudes towards MT technology: Furthermore, in general, integration of MT in translation software is perceived as something useful only by about a half of respondents including both MT users and non- users. The means were very similar:

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