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Optimal and Syntactically-Informed Decoding for Monolingual
Phrase-Based Alignment

McKeown, Kathleen; Thadani, Kapil

The task of aligning corresponding phrases across two related sentences is an important component of approaches for natural language problems such as textual inference, paraphrase detection and text-to-text generation. In this work, we examine a state-of-the-art structured prediction model for the alignment task which uses a phrase-based representation and is forced to decode alignments using an approximate search approach. We propose instead a straightforward exact decoding technique based on integer linear programming that yields order-of-magnitude improvements in decoding speed. This ILP-based decoding strategy permits us to consider syntactically-informed constraints on alignments which significantly increase the precision of the model.



More About This Work

Academic Units
Computer Science
Published Here
April 26, 2013
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