Unsupervised Multilingual Learning for Morphological Segmentation

Proceedings of ACL-08: HLT, pages 737–745, Columbus, Ohio, USA, June 2008. c⃝2008 Association for Computational Linguistics Unsupervised Multilingual Learning for Morphological Segmentation Benjamin Snyder and Regina Barzilay Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology {bsnyder,regina}@csail.mit.edu Abstract For centuries, the deep connection between languages has brought about major discover- ies about human communication. In this pa- per we investigate how this powerful source of information can be exploited for unsuper- vised language learning. In particular, we study the task of morphological segmentation of multiple languages. We present a non- parametric Bayesian model that jointly in- duces morpheme segmentations of each lan- guage under consideration and at the same time identifies cross-lingual morpheme pat- terns, or abstract morphemes. We apply our model to three Semitic languages: Arabic, He- brew, Aramaic, as well as to English. Our results demonstrate that learning morpholog- ical models in tandem reduces error by up to 24% relative to monolingual models. Fur- thermore, we provide evidence that our joint model achieves better performance when ap- plied to languages from the same family. 1 Introduction For centuries, the deep connection between human languages has fascinated linguists, anthropologists and historians (Eco, 1995). The study of this con- nection has made possible major discoveries about human communication: it has revealed the evolu- tion of languages, facilitated the reconstruction of proto-languages, and led to understanding language universals. The connection between languages should be a powerful source of information for automatic lin- guistic analysis as well. In this paper we investi- gate two questions: (i) Can we exploit cross-lingual correspondences to improve unsupervised language learning? (ii) Will this joint analysis provide more or less benefit when the languages belong to the same family? We study these two questions in the context of unsupervised morphological segmentation, the auto- matic division of a word into morphemes (the basic units of meaning). For example, the English word misunderstanding would be segmented into mis - understand - ing. This task is an informative testbed for our exploration, as strong correspondences at the morphological level across various languages have been well-documented (Campbell, 2004). The model presented in this paper automatically induces a segmentation and morpheme alignment from a multilingual corpus of short parallel phrases.1 For example, given parallel phrases meaning in my land in English, Arabic, Hebrew, and Aramaic, we wish to segment and align morphemes as follows: fy arḍ - y b - arṣ - y b - arʿ - y in my land English: Arabic: Hebrew: Aramaic: This example illustrates the potential benefits of unsupervised multilingual learning. The three Semitic languages use cognates (words derived from a common ancestor) to represent the word land. They also use an identical suffix (-y) to represent the first person possessive pronoun (my). These similar- ities in form should guide the model by constraining 1In this paper, we focus on bilingual models. The model can be extended to handle several languages simultaneously as in this example. 737 the space of joint segmentations. The corresponding English phrase lacks …

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