@inproceedings{post-gildea-2008-parsers,
    title = "Parsers as language models for statistical machine translation",
    author = "Post, Matt  and
      Gildea, Daniel",
    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/add-emnlp-2024-awards/2008.amta-papers.16/",
    pages = "172--181",
    abstract = "Most work in syntax-based machine translation has been in translation modeling, but there are many reasons why we may instead want to focus on the language model. We experiment with parsers as language models for machine translation in a simple translation model. This approach demands much more of the language models, allowing us to isolate their strengths and weaknesses. We find that unmodified parsers do not improve BLEU scores over ngram language models, and provide an analysis of their strengths and weaknesses."
}