@inproceedings{sankaran-etal-2012-compact,
    title = "Compact Rule Extraction for Hierarchical Phrase-based Translation",
    author = "Sankaran, Baskaran  and
      Haffari, Gholamreza  and
      Sarkar, Anoop",
    booktitle = "Proceedings of the 10th Conference of the Association for Machine Translation in the Americas: Research Papers",
    month = oct # " 28-" # nov # " 1",
    year = "2012",
    address = "San Diego, California, USA",
    publisher = "Association for Machine Translation in the Americas",
    url = "https://preview.aclanthology.org/fix-sig-urls/2012.amta-papers.16/",
    abstract = "This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model size by 17.8{\%} to 57.6{\%} without impacting translation quality across several language pairs. The Bayesian model is particularly effective for resource-poor languages with evidence from Korean-English translation. To our knowledge, this is the first alternative to Hiero-style rule extraction that finds a more compact synchronous grammar without hurting translation performance."
}