@article{TACL510,
        author = {Rico Sennrich},
        title = {Modelling and Optimizing on Syntactic N-Grams for Statistical
Machine Translation},
        journal = {Transactions of the Association for Computational Linguistics},
        volume = {3},
        year = {2015},
        keywords = {},
        abstract = {The role of language models in SMT is to promote fluent
translation
output, but traditional n-gram language models are unable to capture
fluency phenomena between distant words, such as some morphological
agreement phenomena, subcategorisation, and syntactic collocations with
string-level gaps. Syntactic language models have the potential to fill
this modelling gap. We propose a language model for dependency
structures that is relational rather than configurational and thus
particularly suited for languages with a (relatively) free word order.
It is trainable with Neural Networks, and not only improves over
standard n-gram language models, but also outperforms related syntactic
language models. We empirically demonstrate its effectiveness in terms
of perplexity and as a feature function in string-to-tree SMT from
English to German and Russian. We also show that using a syntactic
evaluation metric to tune the log-linear parameters of an SMT system
further increases translation quality when coupled with a syntactic
language model.},
        issn = {2307-387X},
        url =
{https://tacl2013.cs.columbia.edu/ojs/index.php/tacl/article/view/510},
        pages = {169--182}
}