Paper: Discriminative Sentence Compression With Soft Syntactic Evidence

ACL ID E06-1038
Title Discriminative Sentence Compression With Soft Syntactic Evidence
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

We present a model for sentence com- pression that uses a discriminative large- margin learning framework coupled with a novel feature set defined on compressed bigrams as well as deep syntactic repre- sentations provided by auxiliary depen- dency and phrase-structure parsers. The parsers are trained out-of-domain and con- tain a significant amount of noise. We ar- gue that the discriminative nature of the learning algorithm allows the model to learn weights relative to any noise in the feature set to optimize compression ac- curacy directly. This differs from cur- rent state-of-the-art models (Knight and Marcu, 2000) that treat noisy parse trees, for both compressed and uncompressed sentences, as gold standard when calculat- ing model parameters.