GenDR: A Generic Deep Realizer with Complex Lexicalization

François Lareau, Florie Lambrey, Ieva Dubinskaite, Daniel Galarreta-Piquette, Maryam Nejat

Abstract: We present a generic deep realizer called GenDR, which takes as input an abstract semantic representation of predicate-argument relations, and produces corresponding syntactic dependency structures in English, French, Lithuanian and Persian, with the possibility to fairly easily add more languages. It is generic in that it is designed to operate across a wide range of languages and applications, given the appropriate lexical resources. The focus is on the lexicalization of multiword expressions, with built-in rules to handle thousands of different cross-linguistic patterns of collocations (intensifiers, support verbs, causatives, etc.), and on rich paraphrasing, with the ability to produce many syntactically and lexically varied outputs from the same input. The system runs on a graph transducer, MATE, and its grammar design is directly borrowed from MARQUIS, which we have trimmed down to its core and built upon. The grammar and demo dictionaries are distributed under a CC-BY-SA licence (http://bit.ly/2x8xGVO). This paper explains the design of the grammar, how multiword expressions (especially collocations) are dealt with, and how the syntactic structure is derived from the relative communicative salience of the meanings involved.