Learning to Prune: Context-Sensitive Pruning for Syntactic MT

Wenduan Xu, Yue Zhang, Philip Williams and Philipp Koehn

The 51st Annual Meeting of the Association for Computational Linguistics - Short Papers (ACL Short Papers 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

We present a context-sensitive chart pruning method for cky-style MT decoding. Source phrases that are unlikely to have aligned target constituents are identified using sequence labellers learned from the parallel corpus, and speed-up is obtained by pruning corresponding chart cells. The proposed method is easy to implement, orthogonal to cube pruning and additive to its pruning power. On a large-scale state-of-the-art English-to-German experiment with a string-to-tree model, we obtain a speed-up of more than 60% over a strong baseline, with no loss in BLEU.

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