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    <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.</abstract>
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%0 Conference Proceedings
%T Discriminative, Syntactic Language Modeling through Latent SVMs
%A Cherry, Colin
%A Quirk, Chris
%S Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers
%D 2008
%8 oct 21 25
%I Association for Machine Translation in the Americas
%C Waikiki, USA
%F cherry-quirk-2008-discriminative
%X 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.
%U https://aclanthology.org/2008.amta-papers.4
%P 65-74

Markdown (Informal)

[Discriminative, Syntactic Language Modeling through Latent SVMs](https://aclanthology.org/2008.amta-papers.4) (Cherry & Quirk, AMTA 2008)

• Discriminative, Syntactic Language Modeling through Latent SVMs (Cherry & Quirk, AMTA 2008)

ACL

• Colin Cherry and Chris Quirk. 2008. Discriminative, Syntactic Language Modeling through Latent SVMs. In Proceedings of the 8th Conference of the Association for Machine Translation in the Americas: Research Papers, pages 65–74, Waikiki, USA. Association for Machine Translation in the Americas.