Underspecifying and Predicting Voice for Surface Realisation Ranking

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1007–1017, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Underspecifying and Predicting Voice for Surface Realisation Ranking Sina Zarrieß, Aoife Cahill and Jonas Kuhn Institut f¨ur maschinelle Sprachverarbeitung Universit¨at Stuttgart, Germany {sina.zarriess,aoife.cahill,jonas.kuhn}@ims.uni-stuttgart.de Abstract This paper addresses a data-driven surface realisation model based on a large-scale re- versible grammar of German. We investigate the relationship between the surface realisa- tion performance and the character of the in- put to generation, i.e. its degree of underspec- ification. We extend a syntactic surface reali- sation system, which can be trained to choose among word order variants, such that the can- didate set includes active and passive variants. This allows us to study the interaction of voice and word order alternations in realistic Ger- man corpus data. We show that with an ap- propriately underspecified input, a linguisti- cally informed realisation model trained to re- generate strings from the underlying semantic representation achieves 91.5% accuracy (over a baseline of 82.5%) in the prediction of the original voice. 1 Introduction This paper1 presents work on modelling the usage of voice and word order alternations in a free word order language. Given a set of meaning-equivalent candidate sentences, such as in the simplified En- glish Example (1), our model makes predictions about which candidate sentence is most appropriate or natural given the context. (1) Context: The Parliament started the debate about the state budget in April. a. It wasn’t until June that the Parliament approved it. b. It wasn’t until June that it was approved by the Parliament. c. It wasn’t until June that it was approved. We address the problem of predicting the usage of linguistic alternations in the framework of a surface 1This work has been supported by the Deutsche Forschungs- gemeinschaft (DFG; German Research Foundation) in SFB 732 Incremental specification in context, project D2 (PIs: Jonas Kuhn and Christian Rohrer). realisation ranking system. Such ranking systems are practically relevant for the real-world applica- tion of grammar-based generators that usually gen- erate several grammatical surface sentences from a given abstract input, e.g. (Velldal and Oepen, 2006). Moreover, this framework allows for detailed exper- imental studies of the interaction of specific linguis- tic features. Thus it has been demonstrated that for free word order languages like German, word or- der prediction quality can be improved with care- fully designed, linguistically informed models cap- turing information-structural strategies (Filippova and Strube, 2007; Cahill and Riester, 2009). This paper is situated in the same framework, us- ing rich linguistic representations over corpus data for machine learning of realisation ranking. How- ever, we go beyond the task of finding the correct or- dering for an almost fixed set of word forms. Quite obviously, word order is only one of the means at a speaker’s disposal for expressing some content in a contextually appropriate form; we add systematic alternations like the voice alternation (active vs. pas- sive) to the picture. As an alternative way of …

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