Acceptability Prediction by Means of Grammaticality Quantification

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 57–64, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Acceptability Prediction by Means of Grammaticality Quantification Philippe Blache, Barbara Hemforth & St´ephane Rauzy Laboratoire Parole & Langage CNRS - Universit´e de Provence 29 Avenue Robert Schuman 13621 Aix-en-Provence, France {blache,hemforth,rauzy}@lpl.univ-aix.fr Abstract We propose in this paper a method for quantifying sentence grammaticality. The approach based on Property Grammars, a constraint-based syntactic formalism, makes it possible to evaluate a grammat- icality index for any kind of sentence, in- cluding ill-formed ones. We compare on a sample of sentences the grammaticality indices obtained from PG formalism and the acceptability judgements measured by means of a psycholinguistic analysis. The results show that the derived grammatical- ity index is a fairly good tracer of accept- ability scores. 1 Introduction Syntactic formalisms make it possible to describe precisely the question of grammaticality. When a syntactic structure can be associated to a sen- tence, according to a given grammar, we can de- cide whether or not the sentence is grammatical. In this conception, a language (be it natural or not) is produced (or generated) by a grammar by means of a specific mechanism, for example derivation. However, when no structure can be built, nothing can be said about the input to be parsed except, eventually, the origin of the failure. This is a prob- lem when dealing with non canonical inputs such as spoken language, e-mails, non-native speaker productions, etc. From this perspective, we need robust approaches that are at the same time ca- pable of describing precisely the form of the in- put, the source of the problem and to continue the parse. Such capabilities render it possible to arrive at a precise evaluation of the grammaticality of the input. In other words, instead of deciding on the grammaticality of the input, we can give an indica- tion of its grammaticality, quantified on the basis of the description of the properties of the input. This paper addresses the problem of ranking the grammaticality of different sentences. This ques- tion is of central importance for the understanding of language processing, both from an automatic and from a cognitive perspective. As for NLP, ranking grammaticality makes it possible to con- trol dynamically the parsing process (in choosing the most adequate structures) or to find the best structure among a set of solutions (in case of non- deterministic approaches). Likewise the descrip- tion of cognitive processes involved in language processing by human has to explain how things work when faced with unexpected or non canoni- cal material. In this case too, we have to explain why some productions are more acceptable and easier to process than others. The question of ranking grammaticality has been addressed from time to time in linguistics, without being a central concern. Chomsky, for example, mentioned this problem quite regularly (see for example (Chomsky75)). However he rephrases it in terms of “degrees of ’belonging- ness’ to the …

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