Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Natural Language. Pages 1328-1336.
Abstract
We introduce ( 1) a novel stochastic inversion transduction grammar formalism for bilingual language modeling of sentence-pairs, and (2) the concept of bilingual parsing with potential application to a variety of parallel corpus analysis problems. The formalism combines three tactics against the constraints that render finite-state transducers less useful: it skips directly to a context-free rather than finite-state base, it permits a minimal extra degree of ordering flexibility, and its probabilistic formulation admits an efficient maximum-likelihood bilingual parsing algorithm. A convenient normal form is shown to exist, and we discuss a number of examples of how stochastic inversion transduction grammars bring bilingual constraints to bear upon problematic corpus analysis tasks.
@inproceedings{ijcai1995p171,
title = {Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora},
author = {Wu, Dekai},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1328--1336},
year = {1995},
note = {Natural Language},
url = {https://ijcai.org/proceedings/1995/171},
}
TY - CPAPER TI - Stochastic Inversion Transduction Grammars, with Application to Segmentation, Bracketing, and Alignment of Parallel Corpora AU - Wu, Dekai T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1328 EP - 1336 UR - https://ijcai.org/proceedings/1995/171 ER -Copied
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