Interactive neural machine translation. (September 2017)

• Record Type: Journal Article
• Authors: Peris, Álvaro; Domingo, Miguel; Casacuberta, Francisco
• Is Part Of: Computer speech & language. Volume 45(2017)
• Page Start: 201
• Page End: 220
• Publication Date: 2017-09
• Subjects: Neural machine translation -- Interactive-predictive machine translation -- Recurrent neural networks; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2016.12.003
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: An interactive translation system, based on neural machine translation is deployed. The neural system is adapted to fit within the classical interactive protocol. An additional protocol is presented and implemented with the neural system. Results show that the neural system significantly outperforms classical approaches. The performance is further enhanced by applying the newly proposed protocol. Abstract: Despite the promising results achieved in last years by statistical machine translation, and more precisely, by the neural machine translation systems, this technology is still not error-free. The outputs of a machine translation system must be corrected by a human agent in a post-editing phase. Interactive protocols foster a human–computer collaboration, in order to increase productivity. In this work, we integrate the neural machine translation into the interactive machine translation framework. Moreover, we propose new interactivity protocols, in order to provide the user an enhanced experience and a higher productivity. Results obtained over a simulated benchmark show that interactive neural systems can significantly improve the classical phrase-based approach in an interactive-predictive machine translation scenario.