Maximum entropy models for natural language ambiguity resolution
Maximum entropy models for natural language ambiguity resolution
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Learning to paraphrase: an unsupervised approach using multiple-sequence alignment
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Syntax-based alignment of multiple translations: extracting paraphrases and generating new sentences
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Bootstrapping lexical choice via multiple-sequence alignment
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Automatic alignment of common information in comparable sentences of Portuguese
Companion Proceedings of the XIV Brazilian Symposium on Multimedia and the Web
Multi-sentence compression: finding shortest paths in word graphs
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Generating phrasal and sentential paraphrases: A survey of data-driven methods
Computational Linguistics
Comparing phrase-based and syntax-based paraphrase generation
MTTG '11 Proceedings of the Workshop on Monolingual Text-To-Text Generation
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Multiple sequence alignment techniques have recently gained popularity in the Natural Language community, especially for tasks such as machine translation, text generation, and paraphrase identification. Prior work falls into two categories, depending on the type of input used: (a) parallel corpora (e.g., multiple translations of the same text) or (b) comparable texts (non-parallel but on the same topic). So far, only techniques based on parallel texts have successfully used syntactic information to guide alignments. In this paper, we describe an algorithm for incorporating syntactic features in the alignment process for non-parallel texts with the goal of generating novel paraphrases of existing texts. Our method uses dynamic programming with alignment decision based on the local syntactic similarity between two sentences. Our results show that syntactic alignment outrivals syntax-free methods by 20% in both grammaticality and fidelity when computed over the novel sentences generated by alignment-induced finite state automata.