Reordering Grammar Induction

Milos Stanojevic, Khalil Sima'an

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We present a novel approach for unsupervised induction of a Reordering Grammar using a modified form of permutation trees (Zhang and Gildea, 2007), which we apply to preordering in phrase-based machine translation. Unlike previous approaches, we induce in one step both the hierarchical structure and the transduction function over it from word-aligned parallel corpora. Furthermore, our model (1) handles non-ITG reordering patterns (up to 5-ary branching), (2) is learned from all derivations by treating not only labeling but also bracketing as latent variable, (3) is entirely unlexicalized at the level of reordering rules, and (4) requires no linguistic annotation. Our model is evaluated both for accuracy in predicting target order, and for its impact on translation quality. We report significant performance gains over phrase reordering, and over two known preordering baselines for English-Japanese.
Original languageEnglish
Title of host publicationProceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
Place of PublicationLisbon, Portugal
PublisherAssociation for Computational Linguistics (ACL)
Number of pages11
Publication statusPublished - Sep 2015
Event2015 Conference on Empirical Methods in Natural Language Processing - Lisbon, Portugal
Duration: 17 Sep 201521 Sep 2015


Conference2015 Conference on Empirical Methods in Natural Language Processing
Abbreviated titleEMNLP 2015
Internet address


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