Learning to Actively Learn Neural Machine Translation

Proceedings of the 22nd Conference on Computational Natural Language Learning (CoNLL 2018), pages 334–344 Brussels, Belgium, October 31 - November 1, 2018. c⃝2018 Association for Computational Linguistics 334 Learning to Actively Learn Neural Machine Translation Ming Liu Wray Buntine Faculty of Information Technology, Monash University {ming.m.liu, wray.buntine, gholamreza.haffari} @ monash.edu Gholamreza Haffari Abstract Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a frame- work to learn sentence selection strategies for neural MT. We train the AL query strategy us- ing a high-resource language-pair based on AL simulations, and then transfer it to the low- resource language-pair of interest. The learned query strategy capitalizes on the shared char- acteristics between the language pairs to make an effective use of the AL budget. Our ex- periments on three language-pairs confirms that our method is more effective than strong heuristic-based methods in various conditions, including cold-start and warm-start as well as small and extremely small data conditions. 1 Introduction Parallel training bitext plays a key role in the quality neural machine translation (NMT). Learn- ing high-quality NMT models in bilingually low- resource scenarios is one of the key challenges, as NMT’s quality degrades severely in such setting (Koehn and Knowles, 2017). Recently, the importance of learning NMT models in scarce parallel bitext scenarios has gained attention. Unsupervised approaches try to learn NMT models without the need for par- allel bitext (Artetxe et al., 2017; Lample et al., 2017a). Dual learning/backtranslation tries to start off from a small amount of bilingual text, and leverage monolingual text in the source and tar- get language (Sennrich et al., 2015a; He et al., 2016). Zero/few shot approach attempts to trans- fer NMT learned from rich bilingual settings to low-resource settings (Johnson et al., 2016; Gu et al., 2018). In this paper, we approach this problem from the active learning (AL) perspective. Assuming the availability of an annotation budget and a pool of monolingual source text as well as a small training bitext, the goal is to select the most use- ful source sentences and query their translation from an oracle up to the annotation budget. The queried sentences need to be selected carefully to get the value for the budget, i.e. get the highest improvements in the translation quality of the re- trained model. The AL approach is orthogonal to the aforementioned approaches to bilingually low- resource NMT, and can be potentially combined with them. We present a framework to learn the sentence selection policy most suitable and effective for the NMT task at hand. This is in contrast to the majority of work in AL-MT where hard-coded heuristics are used for query selection (Haffari and Sarkar, 2009; Bloodgood and Callison-Burch, 2010). More concretely, we learn the query pol- icy based on a high-resource language-pair shar- ing similar characteristics with the low-resource …

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