Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation
Kamal Kumar Gupta, Rejwanul Haque, Asif Ekbal, Pushpak Bhattacharyya, Andy Way
Abstract
In interactive machine translation (MT), human translators correct errors in automatic 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 context 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 effort in translation. Furthermore, when we model this source- and target-language syntactic information together as the conditional context, both types complement each other and our fully syntax-informed INMT model statistically significantly reduces human efforts in a French–to–English translation task, 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) reduction in terms of word stroke ratio (WSR) over the baseline.- Anthology ID:
- 2020.eamt-1.21
- Volume:
- Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Lisboa, Portugal
- Editors:
- André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 195–204
- Language:
- URL:
- https://aclanthology.org/2020.eamt-1.21
- DOI:
- Cite (ACL):
- Kamal Kumar Gupta, Rejwanul Haque, Asif Ekbal, Pushpak Bhattacharyya, and Andy Way. 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, pages 195–204, Lisboa, Portugal. European Association for Machine Translation.
- Cite (Informal):
- Modelling Source- and Target- Language Syntactic Information as Conditional Context in Interactive Neural Machine Translation (Gupta et al., EAMT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.eamt-1.21.pdf