Active Learning for Multilingual Statistical Machine Translation

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 181–189, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Active Learning for Multilingual Statistical Machine Translation∗ Gholamreza Haffari and Anoop Sarkar School of Computing Science, Simon Fraser University British Columbia, Canada {ghaffar1,anoop}@cs.sfu.ca Abstract Statistical machine translation (SMT) models require bilingual corpora for train- ing, and these corpora are often multi- lingual with parallel text in multiple lan- guages simultaneously. We introduce an active learning task of adding a new lan- guage to an existing multilingual set of parallel text and constructing high quality MT systems, from each language in the collection into this new target language. We show that adding a new language using active learning to the EuroParl corpus pro- vides a significant improvement compared to a random sentence selection baseline. We also provide new highly effective sen- tence selection methods that improve AL for phrase-based SMT in the multilingual and single language pair setting. 1 Introduction The main source of training data for statistical machine translation (SMT) models is a parallel corpus. In many cases, the same information is available in multiple languages simultaneously as a multilingual parallel corpus, e.g., European Par- liament (EuroParl) and U.N. proceedings. In this paper, we consider how to use active learning (AL) in order to add a new language to such a multilin- gual parallel corpus and at the same time we con- struct an MT system from each language in the original corpus into this new target language. We introduce a novel combined measure of translation quality for multiple target language outputs (the same content from multiple source languages). The multilingual setting provides new opportu- nities for AL over and above a single language pair. This setting is similar to the multi-task AL scenario (Reichart et al., 2008). In our case, the multiple tasks are individual machine translation tasks for several language pairs. The nature of the translation processes vary from any of the source ∗Thanks to James Peltier for systems support for our ex- periments. This research was partially supported by NSERC, Canada (RGPIN: 264905) and an IBM Faculty Award. languages to the new language depending on the characteristics of each source-target language pair, hence these tasks are competing for annotating the same resource. However it may be that in a single language pair, AL would pick a particular sentence for annotation, but in a multilingual setting, a dif- ferent source language might be able to provide a good translation, thus saving annotation effort. In this paper, we explore how multiple MT systems can be used to effectively pick instances that are more likely to improve training quality. Active learning is framed as an iterative learn- ing process. In each iteration new human labeled instances (manual translations) are added to the training data based on their expected training qual- ity. However, if we start with only a small amount of initial parallel data for the new target language, then translation quality is …

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