Dependency-Based N-Gram Models for General Purpose Sentence Realisation

Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 297–304 Manchester, August 2008 Dependency-Based N-Gram Models for General Purpose Sentence Realisation Yuqing Guo NCLT, School of Computing Dublin City University Dublin 9, Ireland yguo@computing.dcu.ie Josef van Genabith NCLT, School of Computing Dublin City University IBM CAS, Dublin, Ireland josef@computing.dcu.ie Haifeng Wang Toshiba (China) Research & Development Center Beijing, 100738, China wanghaifeng@rdc.toshiba.com.cn Abstract We present dependency-based n-gram models for general-purpose, wide- coverage, probabilistic sentence realisa- tion. Our method linearises unordered dependencies in input representations directly rather than via the application of grammar rules, as in traditional chart- based generators. The method is simple, efficient, and achieves competitive accu- racy and complete coverage on standard English (Penn-II, 0.7440 BLEU, 0.05 sec/sent) and Chinese (CTB6, 0.7123 BLEU, 0.14 sec/sent) test data. 1 Introduction Sentence generation,1 or surface realisation can be described as the problem of producing syntacti- cally, morphologically, and orthographically cor- rect sentences from a given semantic or syntactic representation. Most general-purpose realisation systems de- veloped to date transform the input into sur- face form via the application of a set of gram- mar rules based on particular linguistic theories, e.g. Lexical Functional Grammar (LFG), Head- Driven Phrase Structure Grammar (HPSG), Com- binatory Categorial Grammar (CCG), Tree Ad- joining Grammar (TAG) etc. These grammar rules are either carefully handcrafted, as those used in FUF/SURGE (Elhadad, 1991), LKB (Carroll et al., c⃝ 2008. Licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported li- cense (http://creativecommons.org/licenses/by-nc-sa/3.0/). Some rights reserved. 1In this paper, the term “generation” is used generally for what is more strictly referred to by the term “tactical genera- tion” or “surface realisation”. 1999), OpenCCG (White, 2004) and XLE (Crouch et al., 2007), or created semi-automatically (Belz, 2007), or fully automatically extracted from an- notated corpora, like the HPSG (Nakanishi et al., 2005), LFG (Cahill and van Genabith, 2006; Hogan et al., 2007) and CCG (White et al., 2007) resources derived from the Penn-II Treebank (PTB) (Marcus et al., 1993). Over the last decade, probabilistic models have become widely used in the field of natural lan- guage generation (NLG), often in the form of a re- alisation ranker in a two-stage generation architec- ture. The two-stage methodology is characterised by a separation between generation and selection, in which rule-based methods are used to generate a space of possible paraphrases, and statistical meth- ods are used to select the most likely realisation from the space. By and large, two statistical mod- els are used in the rankers to choose output strings: • N-gram language models over different units, such as word-level bigram/trigram mod- els (Bangalore and Rambow, 2000; Langk- ilde, 2000), or factored language models inte- grated with syntactic tags (White et al., 2007). • Log-linear models with different syntactic and semantic features (Velldal and Oepen, 2005; Nakanishi et al., 2005; Cahill et al., 2007). To date, however, probabilistic models learn- ing direct mappings from generation input to sur- face strings, without the effort to construct a gram- mar, have rarely …

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