Learning to Translate in Real-time with Neural Machine Translation

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1053–1062, Valencia, Spain, April 3-7, 2017. c⃝2017 Association for Computational Linguistics Learning to Translate in Real-time with Neural Machine Translation Jiatao Gu †, Graham Neubig ♦, Kyunghyun Cho ‡ and Victor O.K. Li † †The University of Hong Kong ♦Carnegie Mellon University ‡New York University †{jiataogu, vli}@eee.hku.hk ♦gneubig@cs.cmu.edu ‡kyunghyun.cho@nyu.edu Abstract Translating in real-time, a.k.a. simultane- ous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional ma- chine translation methods. We propose a neural machine translation (NMT) frame- work for simultaneous translation in which an agent learns to make decisions on when to translate from the interaction with a pre-trained NMT environment. To trade off quality and delay, we extensively ex- plore various targets for delay and design a method for beam-search applicable in the simultaneous MT setting. Experiments against state-of-the-art baselines on two language pairs demonstrate the efficacy of the proposed framework both quantita- tively and qualitatively.1 1 Introduction Simultaneous translation, the task of translating content in real-time as it is produced, is an im- portant tool for real-time understanding of spoken lectures or conversations (F¨ugen et al., 2007; Ban- galore et al., 2012). Different from the typical machine translation (MT) task, in which transla- tion quality is paramount, simultaneous translation requires balancing the trade-off between transla- tion quality and time delay to ensure that users receive translated content in an expeditious man- ner (Mieno et al., 2015). A number of methods have been proposed to solve this problem, mostly in the context of phrase-based machine translation. These methods are based on a segmenter, which receives the input one word at a time, then decides when to send it to a MT system that translates each 1Code and data can be found at https://github. com/nyu-dl/dl4mt-simul-trans. Last night we served Mr X a beer , who died during the night . < eos > < eos > . ist storben ge-- Nacht der Laufe im der , serviert Bier ein X Herrn wir haben Abend Gestern READ WRITE Figure 1: Example output from the proposed framework in DE → EN simultaneous transla- tion. The heat-map represents the soft alignment between the incoming source sentence (left, up- to-down) and the emitted translation (top, left- to-right). The length of each column represents the number of source words being waited for be- fore emitting the translation. Best viewed when zoomed digitally. segment independently (Oda et al., 2014) or with a minimal amount of language model context (Ban- galore et al., 2012). Independently of simultaneous translation, ac- curacy of standard MT systems has greatly im- proved with the introduction of neural-network- based MT systems (NMT) (Sutskever et al., 2014; Bahdanau et al., 2014). Very recently, there have been a few efforts to apply NMT to simultane- ous translation either through heuristic modifica- tions to the decoding process (Cho and Esipova, 2016), or through the training of an independent …

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