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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation

Gupta, Kamal Kumar, Haque, Rejwanul orcid logoORCID: 0000-0003-1680-0099, Ekbal, Asif, Bhattacharyya, Pushpak and Way, Andy orcid logoORCID: 0000-0001-5736-5930 (2020) Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation. In: Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, 2-6 Nov 2020, Lisboa, Portugal.

Abstract
In interactive machine translation (MT), human translators correct errors in auto- matic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source- language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional con- text for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human efforts in translation. Furthermore, when we model this source- and target-language syntactic information together as the con- ditional context, both types complement each other and our fully syntax-informed INMT model shows statistically significant reduction in human efforts for a French– to–English translation task in a reference- simulated setting, achieving 4.30 points absolute (corresponding to 9.18% relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01% relative) reduc- tion in terms of word stroke ratio (WSR) over the baseline.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Neural Machine Translation; Interactive Neural Machine Translation
Subjects:Computer Science > Computational linguistics
Computer Science > Machine learning
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 22nd Annual Meeting of the European Association for Machine Translation, (EAMT 2020). . European Association for Machine Translation (EAMT).
Publisher:European Association for Machine Translation (EAMT)
Official URL:https://www.aclweb.org/anthology/2020.eamt-1.21
Copyright Information:© 2020 The Authors. (CC-BY-ND-4.0)
Funders:TDIL, MeiTY, Govt. of India for the project ”Hindi to English Machine Translation for Judicial Domain [11(3)/2015-HCC(TDIL], Science Foundation Ireland (SFI) Research Centres Programme (Grant No. 13/RC/2106) and is cofunded under the European Regional Development Fund
ID Code:24420
Deposited On:30 Apr 2020 12:32 by Rejwanul Haque . Last Modified 10 Mar 2021 12:26
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