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arXiv · 1708.01318

The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task

Amr Sharaf·Shi Feng·Khanh Nguyen·Kianté Brantley·Hal Daumé III
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Abstract

We describe the University of Maryland machine translation systems submitted to the WMT17 German-English Bandit Learning Task. The task is to adapt a translation system to a new domain, using only bandit feedback: the system receives a German sentence to translate, produces an English sentence, and only gets a scalar score as feedback. Targeting these two challenges (adaptation and bandit learning), we built a standard neural machine translation system and extended it in two ways: (1) robust reinforcement learning techniques to learn effectively from the bandit feedback, and (2) domain adaptation using data selection from a large corpus of parallel data.

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Amr Sharaf, Shi Feng, Khanh Nguyen, Kianté Brantley, Hal Daumé III. 2017-08-03. The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task. https://arxiv.org/abs/1708.01318

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