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These tools, however, do not use any advanced MT technology and only perform string matching similar Advanced Integration Research normal TM search. Some systems attempted to use more sophisticated techniques to construct target segments from shorter fragments extracted from TM bases.

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These units can then be assembled to suggest a translation for a new segment. Each segment in the source text is also annotated using a POS-tagger.

The idea is to retrieve the fuzzy matches, identify the elements of the source sentence that are not covered by the match, and translate them using SMT techniques. These fuzzy matches and their alignments Advanced Integration Research used to produce a bi-phrase which is then added to the other bi-phrases extracted by the MT system and assigned a strong value to its associated feature in order to prioritise it over other bi-phrases. This methods shows significant improvements compared to both only TM matching and the MT system alone for different fuzzy-match scores. Similar research was carried out by Zhechev and van Genabith,who, instead of alignment matrices, used precise subtree-based alignments of the fuzzy matches retrieved from the TM to determine the correspondeces between the input source sentence and the match.

The parts which are not aligned are then also translated with an SMT system. The offline method consists in applying MT before the translation for the segments which did not match with any segment in the TM. These source segments are translated with an MT engine and then added to the project as another TM file or merged with an existing TM.

In order to account for the MT quality, which is lower than the human translation quality in a normal TM, MT-generated TMs are sometimes assigned penalty scores. Finally, the translator gets these results along with other suggestions.

On the other hand, the online method allows the translator to see MT suggestions directly produced for the current segment via a plugin or an API. The translator is free to use these suggestions or discard them depending on their quality. The problem with this method is that it is not easy to decide which position in the ranking these suggestions should appear on, and what match percentage they should be equivalent to. The offline method, in its turn, is more time-consuming and adds additional stages in the project preparation process. An attempt to compare these two workflows was carried out by Kanavos and Kartsaklis, The authors Advanced Integration Research productivity in different translation setups: They report significant productivity gains in all workflows which included MT.

This is not, however, properly demonstrated, as the two approaches, even though Advanced Integration Research with the same MT engine Moseswere carried out with different CAT tools. In addition, no direct comparison with a quantitative measure like, for instance, translation speed in terms of words per hour is provided in the paper. Smilar works include Koehn and Senellart, Advanced Integration Research, Simard and Isabelle,Kranias and Samiotou,among others. One of the drawbacks that they have in common is that they normally use automatic metrics such as BLEU Papineni et al. Even though some of these metrics have shown high correlation with the human judgment, it is not clear to which extent these systems can be useful for translators, i.

However, this technology was not well adopted, probably due to the low quality provided by the MT systems, and was neglected in subsequent software versions.

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The research conducted by Federico et al. They authors used a popular CAT tool and the MyMemory4 plugin, which retrieves suggestions from a publicly available TM Advanced Integration Research and, when no TM match is available, it provides suggestions from the Google Translate system.


The plugin also records translation time for each segment, which allows to measure translation speed. The other productivity indicator used in the experiment was post-editing effort, which is computed as the edit distance between the suggestion and the final translation. Using MT led to productivity gains for all translators. However, the results varied significantly among translators depending on their working style some translators correct more including subtle stylistical errors, while others only performed light post-editing and the configuration of their user interface. On the other hand, this setting introduces some factors, which make the experiment less controlled Advanced Integration Research introduces significant variation in Advanced Integration Research results. A problem that has not been discussed in much detail in this context is how exactly to present the MT suggestions generated by the plugin among the suggestions from other sources like TM, term bases, glossaries.

How do we rank the MT results relative to other suggested segments or, in other words, what percentage should be assigned to them? Can these scores be calculated using quality estimation techniques?

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And finally, will this score depend on the language pair? It consists in suggesting words or 79 phrases to complete the segment that the translator has started typing. The Advanced Integration Research, if she wants to discard the suggestions, can simply overtype them, or accept them. However, to our knowledge only few commercial CAT tools use MT techniques to generate autocompletion suggestions.

It describes the MT suggestions in the Autosuggest feature as one of the main novalties of this new version of the software5. In research, the first attempt to use MT in autocompletion was made by Langlais et al. The system provides suggestions in real time while the user is typing. Advanced Integration Research 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 Advanced Integration Research addition to other ITS and advanced traffic management.

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