Multi-Engine Machine Translation with Voted Language Model

Multi-Engine Machine Translation with Voted Language Model Tadashi Nomoto National Institute of Japanese Literature 1-16-10 Yutaka Shinagawa Tokyo 142-8585 Japan nomoto@acm.org Abstract The paper describes a particular approach to multi- engine machine translation (MEMT), where we make use of voted language models to selectively combine translation outputs from multiple off-the- shelf MT systems. Experiments are done using large corpora from three distinct domains. The study found that the use of voted language models leads to an improved performance of MEMT sys- tems. 1 Introduction As the Internet grows, an increasing number of commercial MT systems are getting on line ready to serve anyone anywhere on the earth. An inter- esting question we might ponder is whether it is not possible to aggregate the vast number of MT sys- tems available on the Internet into one super MT which surpasses in performance any of those MTs that comprise the system. And this is what we will be concerned with in the paper, with somewhat watered-down settings. People in the speech community pursued the idea of combining off-the-shelf ASRs (automatic speech recognizers) into a super ASR for some time, and found that the idea works (Fiscus, 1997; Schwenk and Gauvain, 2000; Utsuro et al., 2003). In IR (in- formation retrieval), we find some efforts going (un- der the name of distributed IR or meta-search) to se- lectively fuse outputs from multiple search engines on the Internet (Callan et al., 2003). So it would be curious to see whether we could do the same with MTs. Now back in machine translation, we do find some work addressing such concern: Frederking and Nirenburg (1994) develop a multi-engine MT or MEMT architecture which operates by com- bining outputs from three different engines based on the knowledge it has about inner workings of each of the component engines. Brown and Fred- erking (1995) is a continuation of Frederking and Nirenburg (1994) with an addition of a ngram- based mechanism for a candidate selection. Nomoto (2003), however, explores a different line of re- search whose goal is to combine black box MTs us- ing statistical confidence models. Similar efforts are also found in Akiba et al. (2002). The present paper builds on the prior work by Nomoto (2003). We start by reviewing his ap- proach, and go on to demonstrate that it could be im- proved by capitalizing on dependence of the MEMT model there on language model. Throughout the paper, we refer to commercial black box MT sys- tems as OTS (off-the-shelf) systems, or more sim- ply, OTSs. 2 Confidence Models We take it here that the business of MEMT is about choosing among translation outputs from multiple MT systems, whether black box or not, for each in- put text. Therefore the question we want to address is, how do we go about choosing among MT outputs so that we end up with a best one? What we propose to do is to use some confidence models for translations generated by OTSs, and let them decide …

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