Don’t Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1342–1352, October 25-29, 2014, Doha, Qatar. c⃝2014 Association for Computational Linguistics Don’t Until the Final Verb Wait: Reinforcement Learning for Simultaneous Machine Translation Alvin C. Grissom II and Jordan Boyd-Graber Computer Science University of Colorado Boulder, CO Alvin.Grissom@colorado.edu Jordan.Boyd.Graber@colorado.edu He He, John Morgan, and Hal Daum´e III Computer Science and UMIACS University of Maryland College Park, MD {hhe,jjm,hal}@cs.umd.edu Abstract We introduce a reinforcement learning- based approach to simultaneous ma- chine translation—producing a trans- lation while receiving input words— between languages with drastically dif- ferent word orders: from verb-final lan- guages (e.g., German) to verb-medial languages (English). In traditional ma- chine translation, a translator must “wait” for source material to appear be- fore translation begins. We remove this bottleneck by predicting the final verb in advance. We use reinforcement learn- ing to learn when to trust predictions about unseen, future portions of the sentence. We also introduce an evalua- tion metric to measure expeditiousness and quality. We show that our new translation model outperforms batch and monotone translation strategies. 1 Introduction We introduce a simultaneous machine transla- tion (MT) system that predicts unseen verbs and uses reinforcement learning to learn when to trust these predictions and when to wait for more input. Simultaneous translation is producing a par- tial translation of a sentence before the input sentence is complete, and is often used in im- portant diplomatic settings. One of the first noted uses of human simultaneous interpreta- tion was the Nuremberg trials after the Sec- ond World War. Siegfried Ramler (2009), the Austrian-American who organized the transla- tion teams, describes the linguistic predictions and circumlocutions that translators would use to achieve a tradeoff between translation la- tency and accuracy. The audio recording tech- nology used by those interpreters sowed the seeds of technology-assisted interpretation at the United Nations (Gaiba, 1998). Performing real-time translation is especially difficult when information that comes early in the target language (the language you’re trans- lating to) comes late in the source language (the language you’re translating from). A common example is when translating from a verb-final (sov) language (e.g., German or Japanese) to a verb-medial (svo) language, (e.g., English). In the example in Figure 1, for instance, the main verb of the sentence (in bold) appears at the end of the German sentence. An of- fline (or “batch”) translation system waits until the end of the sentence before translating any- thing. While this is a reasonable approach, it has obvious limitations. Real-time, interactive scenarios—such as online multilingual video conferences or diplomatic meetings—require comprehensible partial interpretations before a sentence ends. Thus, a significant goal in interpretation is to make the translation as expeditious as possible. We present three components for an sov-to- svo simultaneous mt system: a reinforcement learning framework that uses predictions to create expeditious translations (Section 2), a system to predict how a sentence will end (e.g., predicting the main verb; Section 4), and a met- ric that balances quality …

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