Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 113–124 Copenhagen, Denmark, September 7–11, 2017. c⃝2017 Association for Computational Linguistics Past, Present, Future: A Computational Investigation of the Typology of Tense in 1000 Languages Ehsaneddin Asgari1,2 and Hinrich Sch¨utze1 1Center for Information and Language Processing, LMU Munich, Germany 2Applied Science and Technology, University of California, Berkeley, CA, USA, asgari@berkeley.edu inquiries@cislmu.org Abstract We present SuperPivot, an analysis method for low-resource languages that occur in a superparallel corpus, i.e., in a corpus that contains an order of magni- tude more languages than parallel corpora currently in use. We show that SuperPivot performs well for the crosslingual analysis of the linguistic phenomenon of tense. We produce analysis results for more than 1000 languages, conducting – to the best of our knowledge – the largest crosslin- gual computational study performed to date. We extend existing methodology for leveraging parallel corpora for typological analysis by overcoming a limiting as- sumption of earlier work: We only require that a linguistic feature is overtly marked in a few of thousands of languages as opposed to requiring that it be marked in all languages under investigation. 1 Introduction Significant linguistic resources such as machine- readable lexicons and part-of-speech (POS) tag- gers are available for at most a few hundred lan- guages. This means that the majority of the languages of the world are low-resource. Low- resource languages like Fulani are spoken by tens of millions of people and are politically and eco- nomically important; e.g., to manage a sudden refugee crisis, NLP tools would be of great ben- efit. Even “small” languages are important for the preservation of the common heritage of hu- mankind that includes natural remedies and lin- guistic and cultural diversity that can potentially enrich everybody. Thus, developing analysis methods for low-resource languages is one of the most important challenges of NLP today. We address this challenge by proposing a new method for analyzing what we call superparallel corpora, corpora that are by an order of magnitude more parallel than corpora that have been available in NLP to date. The corpus we work with in this paper is the Parallel Bible Corpus (PBC) that con- sists of translations of the New Testament in 1169 languages. Given that no NLP analysis tools are available for most of these 1169 languages, how can we extract the rich information that is poten- tially hidden in such superparallel corpora? The method we propose is based on two hy- potheses. H1 Existence of overt encoding. For any important linguistic distinction f that is fre- quently encoded across languages in the world, there are a few languages that encode f overtly on the surface. H2 Overt-to-overt and overt-to- non-overt projection. For a language l that en- codes f, a projection of f from the “overt lan- guages” to l in the superparallel corpus will iden- tify the encoding that l uses for f, both in cases in which the encoding that l uses is overt and in …

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