A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar

Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 328–335, Prague, Czech Republic, June 2007. c⃝2007 Association for Computational Linguistics A Symbolic Approach to Near-Deterministic Surface Realisation using Tree Adjoining Grammar Claire Gardent CNRS/LORIA Nancy, France claire.gardent@loria.fr Eric Kow INRIA/LORIA/UHP Nancy, France eric.kow@loria.fr Abstract Surface realisers divide into those used in generation (NLG geared realisers) and those mirroring the parsing process (Reversible re- alisers). While the first rely on grammars not easily usable for parsing, it is unclear how the second type of realisers could be param- eterised to yield from among the set of pos- sible paraphrases, the paraphrase appropri- ate to a given generation context. In this pa- per, we present a surface realiser which com- bines a reversible grammar (used for pars- ing and doing semantic construction) with a symbolic means of selecting paraphrases. 1 Introduction In generation, the surface realisation task consists in mapping a semantic representation into a grammati- cal sentence. Depending on their use, on their degree of non- determinism and on the type of grammar they as- sume, existing surface realisers can be divided into two main categories namely, NLG (Natural Lan- guage Generation) geared realisers and reversible realisers. NLG geared realisers are meant as modules in a full-blown generation system and as such, they are constrained to be deterministic: a generation system must output exactly one text, no less, no more. In or- der to ensure this determinism, NLG geared realisers generally rely on theories of grammar which sys- tematically link form to function such as systemic functional grammar (SFG, (Matthiessen and Bate- man, 1991)) and, to a lesser extent, Meaning Text Theory (MTT, (Mel’cuk, 1988)). In these theories, a sentence is associated not just with a semantic rep- resentation but with a semantic representation en- riched with additional syntactic, pragmatic and/or discourse information. This additional information is then used to constrain the realiser output.1 One drawback of these NLG geared realisers however, is that the grammar used is not usually reversible i.e., cannot be used both for parsing and for generation. Given the time and expertise involved in developing a grammar, this is a non-trivial drawback. Reversible realisers on the other hand, are meant to mirror the parsing process. They are used on a grammar developed for parsing and equipped with a compositional semantics. Given a string and such a grammar, a parser will assign the input string all the semantic representations associated with that string by the grammar. Conversely, given a seman- tic representation and the same grammar, a realiser will assign the input semantics all the strings as- sociated with that semantics by the grammar. In such approaches, non-determinism is usually han- dled by statistical filtering: treebank induced prob- abilities are used to select from among the possible paraphrases, the most probable one. Since the most probable paraphrase is not necessarily the most ap- propriate one in a given context, it is unclear how- ever, how such realisers could be integrated into a generation system. …

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