A trainable transfer-based MT approach for languages with limited resources

116 A Trainable Transfer-based Machine Translation Approach for Languages with Limited Resources Alon Lavie, Katharina Probst, Erik Peterson, Stephan Vogel, Lori Levin, Ariadna Font-Llitjos and Jaime Carbonell Language Technologies Institute Carnegie Mellon University Pittsburgh, PA, USA Email: alavie@cs.cmu.edu Abstract. We describe a Machine Translation (MT) approach that is specifically designed to enable rapid development of MT for languages with limited amounts of online resources. Our approach assumes the availability of a small number of bi-lingual speakers of the two languages, but these need not be linguistic experts. The bi-lingual speakers create a comparatively small corpus of word aligned phrases and sentences (on the order of magnitude of a few thousand sentence pairs) using a specially designed elicitation tool. From this data, the learning module of our system automatically infers hierarchical syntactic transfer rules, which encode how syntactic constituent structures in the source language transfer to the target language. The collection of transfer rules is then used in our run-time system to translate previously unseen source language text into the target language. We describe the general principles underlying our approach, and present results from an experiment, where we developed a basic Hindi-to-English MT system over the course of two months, using extremely limited resources. 1. Introduction Corpus-based Machine Translation (MT) approaches such as Statistical Machine Translation (SMT) (Brown et al, 1990), (Brown et al, 1993), (Vogel and Tribble, 2002), (Yamada and Knight, 2001), (Papineni et al, 1998), (Och and Ney, 2002) and Example-based Machine Translation (EBMT) (Brown, 1997), (Sato and Nagao, 1990) have received much attention in recent years, and have significantly improved the state-of-the-art of Machine Translation for a number of different language pairs. These approaches are attractive because they are fully automated, and require orders of magnitude less human labor than traditional rule- based MT approaches. However, to achieve reasonable levels of translation performance, the corpus-based methods require very large volumes of sentence-aligned parallel text for the two languages – on the order of magnitude of a million words or more. Such resources are currently available for only a small number of language pairs. While the amount of online resources for many languages will undoubtedly grow over time, many of the languages spoken by smaller ethnic groups and populations in the world will not have such resources within the foreseeable future. Corpus-based MT approaches will therefore not be effective for such languages for some time to come. Our MT research group at Carnegie Mellon, under DARPA and NSF funding, has been working on a new MT approach that is specifically designed to enable rapid development of MT for languages with limited amounts of online resources. Our approach assumes the availability of a small number of bi-lingual speakers of the two languages, but these need not be linguistic experts. The bi-lingual speakers create a comparatively small corpus of word aligned phrases and sentences (on the order of magnitude of a few thousand sentence pairs) using a specially designed elicitation tool. From this data, the learning module of our system automatically …

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