MACHINE TRANSLATABILITY OF RUSSIAN SCIENTIFIC TEXTS
Keywords:
machine translatability, translatability indicator, machine translation, scientific text, corpusAbstract
The problem of the identification of text parameters having negative impact on the
quality of machine translation is considered in the article. Definition and classification of
translatability indicators is given. Linguistic phenomena in the scientific texts in Russian
are considered at the graphic, lexical and syntagmatic levels. The special attention is paid
to textual features causing problems for machine translation at the syntagmatic level, including
both universal features and ones specific to Russian. A classification of formal machine
translatability indicators for Russian scientific texts is compiled on the basis of language
features analysis. The distinguished classes are matched against text translation
problems associated with the corresponding indicator. The obtained results can be applied
in translation practice and when developing tools for computational support of translation.
References
Дрейфус Х. Чего не могут вычислитель-
ные машины: Критика искусственного разума. М.:
Прогресс, 1978. 334 с. [Dreyfus H.L. Chego ne mogut
vyschislitelnye machiny: Kritika iskusstvennogo
razuma (What Computers Still Can’t Do: A Critique
of Artificial Reason). Moscow, Progress Publ., 1978,
p.].
Шереметьева С.О. Интерактивное рефери-
рование, ориентированной на машинный перевод.
Вестник ЮУрГУ. Серия «Лингвистика». 2013. Т.
№ 1. С. 89–92. [Sheremetyeva S.O. Interaktivnoe
referirovanie, orientirovannoe na machinniy perevod
(Machine Translation Oriented Interactive Summarization),
Vestnik Yuzhno-Uralskogo gosudarstvennogo
universiteta, seriya Lingvistika (The Bulletin of South
Ural State University, Ser. Linguistics), 2013, vol. 10,
no. 1, pp. 89–92].
Allen J., Hogan C. Toward the Development
of a Post-Editing Module for Raw Machine Translation
Output: A Controlled Language Perspective, Proceedings
of the Third International Workshop on Controlled
Language Applications (Seattle, WA), 2000,
pp. 62–71.
Arnold D., Balkan L., Maijer L., Humphreys
R. L., Sadler L. Machine Translation: An Introductory
Guide, Oxford, NCC Blackwell, 1994, 206 p.
Bergsma S., Yarowsky D., Church K. Using
Large Monolingual and Bilingual Corpora to Improve
Coordination Disambiguation, Proceedings of the 49th
Annual Meeting of the Association for Computational
Linguistics (Portland, Oregon), 2011, pp. 1346–1355.
Bernth A., McCord M. The Effect of Source
Analysis on Translation Confidence, J.S. White (ed.)
Envisioning Machine Translation in the Information
Future: 4th Conference of the Association for Machine
Translation in the Americas (AMTA 2000) (Cuernavaca,
Mexico), Berlin, Springer, 2000, pp. 89–99.
Doyon J., Doran C., Means C. D., Parr D.
Automated Machine Translation Improvement
through Post-editing Techniques: Analyst and Translator
Experiments, Proceedings of AMTA, 2008, pp.
–353.
Gdaniec C. The Logos Translatability Index,
Technology Partnerships for Crossing the Language
Barrier: Proceedings of the First Conference of the
Association for Machine Translation in the Americas
AMTA, Oct. 1994, pp. 97–105.
Goldberg M. An Unsupervised Model for
Statistically Determining Coordinate Phrase Attachment,
Proceedings of ACL, 1999, pp. 610–614.
Hartley A., Tatsumi M. , Isahara H., Kageura
K., Miyata R. Readability and Translatability
Judgments for ‘Controlled Japanese’, Proceedings of
the 16th EAMT Conference (Trento, Italy), May 2012,
pp. 237–244.
Hutchins W. J., Somers L. An Introduction
to Machine Translation, London, Academic Press,
, 362 p.
Kittredge R. Sublanguages and Controlled
Language, R. Mitkov (ed.) The Oxford Handbook of
Computational Linguistics, Oxford, OUP, 2003,
pp. 430–447.
Knight K., Chander I. Automated Post-
Editing of Documents, Proceedings of the 12th National
Conference on Artificial Intelligence (Seattle,
WA), 1994, pp. 779–784.
Marinčič D. Parding with Intraclausal
Coordination and Clause Detection, Informatitica,
, no. 34, pp. 263–264.
Nakov P., Hearst M. Using the Web as an
Implicit Training Set: Application to Structural Ambiguity
Resolution, Proceedings of HLT-EMNLP, 2005,
pp. 835–842.
Nyberg E., Mitamura T., Huijsen W.-O.
Controlled Language for Authoring and Translation,
H. Somers (ed.), Computers and Translation, Amsterdam,
NL, Benjamins, 2003, pp. 245–281.
O’Brien S. Methodologies for Measuring
the Correlations between Post-Editing Effort and Machine
Translatability, Machine Translation, 2005,
no. 19, pp. 37–58.
O'Brien S. Controlling Controlled English:
An Analysis of Several Controlled Language Rule
Sets, Joint conference combining the 8th International
Workshop of the European Association for Machine
Translation and the 4th Controlled Language Applications
Workshop (EAMT/CLAW)(Dublin City University),
May 2003, pp. 105–114.19. Okuma H., Hara K., Shimbo M., Matsumoto
Y. Bypassed Alignment Graph for Learning Coordination
in Japanese Sentences, Proceedings of the
ACL-IJCNLP 2009: Conference Short Papers (Suntec,
Singapore), Aug. 2009, pp. 5–8.
Okumura A., Muraki K. Symmetric Pattern
Matching Analysis for English Coordinate Structures,
Proceedings of the fourth conference on Applied Natural
Language Processing (ANLP), 1994, pp. 41–46.
Pedro R. de. The Translatability of Texts: A
Historical Overview, Meta, 1999, Vol. XLIV, no. 4,
pp. 546–559.
Povlsen C., Underwood N., Music B., Neville
A. Evaluating Text-type Suitability for Machine
Translation a Case Study on an English-Danish MT
System, A. Rubio, N. Gallardo, R. Castro & A. Tejada
(eds.) Proceedings of the First International Conference
on Language Resources and Evaluation (Granada,
Spain), 1998, vol. 1, pp. 27–31.
Reuther U. Two in one – Can it work? Readability
and Translatability by means of Controlled
Language, Proceedings of EAMT-CLAW03, Controlled Language Translation (Dublin City University,
Dublin), May2003, pp. 124–132.
Roh Y.-H., Lee K.-Y., Choi S.-K., Kwon
Oh-W., Kim Y.-G. Recognizing Coordinate Structures
for Machine Translation of English Patent
Documents, Proceedings of the 22nd Pacific Asia
Conference on Language, Information and Computation
(De La Salle University, Manila, Philippines),
Nov. 2008, pp. 460–466.
Roh Y.-H., Seo Y.-A., Lee K.-Y., Choi S.-K.
Long Sentence Partitioning using Structure Analysis
for Machine Translation, Proceedings of the Sixth
Natural Language Processing Pacific Rim Symposium
(Hitotsubashi Memorial Hall, National Center of
Sciences, Tokyo, Japan), Nov. 2001, pp. 646–652.
Sheremetyeva S. Handling Low Translatability
in Machine Translation, Proceedings of the Eleventh
Conference of European Association of Machine
Translation (EAMT)(Oslo, Norway), June 2006,
pp. 105–114.
Trujillo A. Translation Engines: Techniques
for Machine Translation, London, Sptringer-Verlag,
, 303 p.
Underwood N. L., Jongejan B. Translatability
Checker: A Tool to Help Decide Whether to Use MT,
B. Maegaard (ed.), Proceedings of MT Summit VIII
(Santiago de Compostela, Galicia, Spain), Sept. 2001,
pp. 363–368.
