3. Survey content
CAT tools - usage
- best and worst features
Machine Translation - usage
- integration in CAT
- languages and domains
Corpora - using
- compiling
- special tools
Terminology - management
- extraction
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5. Features of CAT tools
- Sentence alignment
- Automa0c handling
of tags
- Real-0me preview - Edit TM
- Autopropaga0on
- OCR
- Speech recogni0on
“Just make it
simple”
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6. Machine Translation
∙ 36% of MT users
∙ 74% find good MT useful
∙ 1/3 of respondents did not
know if they had MT in their
CAT tool
∙ one of the least useful
features in CAT
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7. Post-editing of MT
Can PE be a way to improve the MT experience for a professional
translator?
MT errors in post-editing
1. take into account when evaluating MT output
2. automatically post-edit certain errors
3. highlight most difficult errors
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8. Corpora
- 15% of corpora users
- compiling corpora is time consuming (57%); not familiar with any
special tools (34%)
but:
concordance search is favourite feature
∙ make it easy to incorporate more resources in concordance search
(not only TM)
∙ parallel and comparable corpora
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9. Terminology
Appears both in the favourite and most-hated features =>
terminology management is necessary, but the current tools are not
good enough.
- Study different steps in the terminology management process to
make it easier
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10. Bibliography
Costa, H., Corpas-Pastor, G., Seghiri, M., and Zaretskaya, A. (2016). Nine terminology extraction tools - Are they useful for translators?
Multilingual.
Zaretskaya, A. (2016). A quantitative method for evaluation of cat tools based on user preferences. In Proceedings of the AELFE XV
International Conference. University of Alcalá.
Zaretskaya, A., Corpas Pastor, G., and Seghiri, M. (2015a). Translators’ requirements for translation technologies: a user survey. In
Corpas-Pastor, G., Seghiri-Domínguez, M., Gutiérrez-Florido, R., and na, M. U.-M., editors, Nuevos horizontes en los Estudios de
Traducción e Interpretación (Trabajos completos) / New Horizons in Translation and Interpreting Studies (Full papers) / Novos
horizontes dos Estudos da Tradução e Interpretação (Comunicações completas), pages 247–254, Malaga, Spain. AIETI, Tradulex.
Zaretskaya, A., Corpas-Pastor, G., and Seghiri, M. (2016a). Corpora in computer-assisted translation: a users’ view. In Corpas-Pastor, G. and
Seghiri, M., editors, Corpus-based Approaches to Translation and Interpreting: from theory to applications. Peter Lang, Frankfurt.
Zaretskaya, A., Corpas-Pastor, G., and Seghiri-Domínguez, M. (2016b). A quality evaluation template for machine translation. Translation
Journal.
Zaretskaya, A., Pastor, G. C., and Seghiri, M. (2015b). Integration of machine translation in CAT tools: State of the art, evaluation and user
attitudes. SKASE Journal for Translation and Interpretation, 8(1):76–88.
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12. From the horse’s mouth
Agencies using MT are … only interested in money, not in quality and it is our duty to inform end clients about this greedy attitude.
[CAT tools] should be made simple to operate for a linguist not for an IT specialist
I would like to see more compatibility between systems
Unfortunately, many people think that Google translator, or Babelfish, are viable alternatives to paying a human translator.
Make everything easier and easier by making the usage limitless. Thanks.
I feel there is industry pressure now for more translation work to be done on CAT software so perhaps I am a bit of a dinosaur in my
attitude!
CAT or TMs are incomprehensible and un-learnable
TM tools are just stealing knowledge from translators and reducing their earnings. I hate them. Thank you.
This survey teaches me that all those tools exist! I wake up to a new world of possibilities!
It would be useful t have a tool that adapts to my ”style”... not only in its terminology but also in its grammatical twists
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