@inproceedings{cherry-quirk-2008-discriminative,
    title = "Discriminative, Syntactic Language Modeling through Latent {SVM}s",
    author = "Cherry, Colin  and
      Quirk, Chris",
    booktitle = "Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers",
    month = oct # " 21-25",
    year = "2008",
    address = "Waikiki, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2008.amta-papers.4/",
    pages = "65--74",
    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 a part-of-speech dictionary and binary-labeled sentences. We follow the paradigm of discriminative language modeling with pseudo-negative 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."
}