A systematic comparison of various statistical alignment models
Computational Linguistics
HMM-based word alignment in statistical translation
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 2
Learning as search optimization: approximate large margin methods for structured prediction
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A phrase-based alignment model for natural language inference
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NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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Journal of Artificial Intelligence Research
Learning alignments and leveraging natural logic
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Distributed training strategies for the structured perceptron
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Title generation with quasi-synchronous grammar
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Towards strict sentence intersection: decoding and evaluation strategies
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
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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.