Output details
11 - Computer Science and Informatics
University of Edinburgh
Output title
A Gibbs sampler for phrasal synchronous grammar induction
Type
E - Conference contribution
DOI
-
Name of conference/published proceedings
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2
Volume number
-
Issue number
-
First page of article
782
ISSN of proceedings
-
Year of publication
2009
Number of additional authors
3
Additional information
<22> Originality: We show how the heuristics associated with creating statistical translation grammars (an approach used by virtually all machine translation researchers) can be removed using modern Bayesian non-parametric methods. Additionally we show how Bayesian inference can be made to scale to larger corpora than before.
Significance: Statistical Machine Translation is driven by heuristics. We show how these heuristics can be cleanly removed using a prior over grammars. This more strongly relates translation with machine learning.
Rigour: Uses optimisation on a training set and evaluation on a test set. Paper appeared in the top NLP venue (acceptance rate 21%).
Interdisciplinary
-
Cross-referral requested
-
Research group
D - Institute for Language, Cognition & Computation
Citation count
-
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
