@inproceedings{cherry2008discriminative,
author = {Cherry, Colin and Quirk, Chris},
title = {Discriminative, Syntactic Language Modeling through Latent SVMs},
booktitle = {Proceeding of AMTA},
year = {2008},
month = {October},
abstract = {We construct a discriminative, syntactic language model (LM) by using a latent support vector machine (SVM) to train an unlexicalized parser to judge sentences. That is, the parser is optimized so that correct sentences receive high-scoring trees, while incorrect sentences do not. Because of this alternative objective, the parser can be trained with only apart-of-speech dictionary and binary-labeled sentences. We follow the paradigm of discriminative language modeling with pseudonegative examples (Okanohara and Tsujii, 2007), and demonstrate significant improvements in distinguishing real sentences from pseudo-negatives. We also investigate the related task of separating machine-translation (MT) outputs from reference translations, again showing large improvements. Finally, we test our LM in MT reranking, and investigate the language-modeling parser in the context of unsupervised parsing.},
publisher = {Association for Machine Translation in the Americas},
url = {http://approjects.co.za/?big=en-us/research/publication/discriminative-syntactic-language-modeling-through-latent-svms/},
edition = {Proceeding of AMTA},
}