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Application-driven statistical paraphrase generation

Authors:

Sheng LiAuthors Info & Claims

ACL '09: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

Pages 834 - 842

Published: 02 August 2009 Publication History

Abstract

Paraphrase generation (PG) is important in plenty of NLP applications. However, the research of PG is far from enough. In this paper, we propose a novel method for statistical paraphrase generation (SPG), which can (1) achieve various applications based on a uniform statistical model, and (2) naturally combine multiple resources to enhance the PG performance. In our experiments, we use the proposed method to generate paraphrases for three different applications. The results show that the method can be easily transformed from one application to another and generate valuable and interesting paraphrases.

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Cited By

Siddique AOymak SHristidis VGupta RLiu YShah MRajan STang JPrakash B(2020)Unsupervised Paraphrasing via Deep Reinforcement LearningProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403231(1800-1809)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403231
Ray AShen YJin H(2018)Learning out-of-vocabulary words in intelligent personal agentsProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304369(4309-4315)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304369
Huang JZhao SDing SWu HSun MWang H(2016)Generating recommendation evidence using translation modelProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3061014(2810-2816)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3061014
Show More Cited By

Index Terms

Application-driven statistical paraphrase generation
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics

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ACL '09: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - Volume 2

August 2009

595 pages

ISBN:9781932432466

General Chair:
Keh-Yih Su
Behavior Design Corp., Taiwan

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 02 August 2009

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Overall Acceptance Rate 85 of 443 submissions, 19%

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Cited By

Siddique AOymak SHristidis VGupta RLiu YShah MRajan STang JPrakash B(2020)Unsupervised Paraphrasing via Deep Reinforcement LearningProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3394486.3403231(1800-1809)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1145/3394486.3403231
Ray AShen YJin H(2018)Learning out-of-vocabulary words in intelligent personal agentsProceedings of the 27th International Joint Conference on Artificial Intelligence10.5555/3304222.3304369(4309-4315)Online publication date: 13-Jul-2018
https://dl.acm.org/doi/10.5555/3304222.3304369
Huang JZhao SDing SWu HSun MWang H(2016)Generating recommendation evidence using translation modelProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3061014(2810-2816)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3061014
Bouamor HMax AVilnat A(2013)Multitechnique paraphrase alignmentACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836774:3(1-27)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483677
Burrows SPotthast MStein B(2013)Paraphrase acquisition via crowdsourcing and machine learningACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836764:3(1-21)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483676
Cohn TLapata M(2013)An abstractive approach to sentence compressionACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836744:3(1-35)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483674
Marton Y(2013)Distributional phrasal paraphrase generation for statistical machine translationACM Transactions on Intelligent Systems and Technology10.1145/2483669.24836724:3(1-32)Online publication date: 1-Jul-2013
https://dl.acm.org/doi/10.1145/2483669.2483672
Max ABouamor HVilnat ATsujii JHenderson JPasca M(2012)Generalizing sub-sentential paraphrase acquisition across original signal type of text pairsProceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning10.5555/2390948.2391027(721-731)Online publication date: 12-Jul-2012
https://dl.acm.org/doi/10.5555/2390948.2391027
Sun HZhou MLi HLin COsborne M(2012)Joint learning of a dual SMT system for paraphrase generationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 210.5555/2390665.2390675(38-42)Online publication date: 8-Jul-2012
https://dl.acm.org/doi/10.5555/2390665.2390675
Wubben Svan den Bosch AKrahmer ELi HLin COsborne M(2012)Sentence simplification by monolingual machine translationProceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 110.5555/2390524.2390660(1015-1024)Online publication date: 8-Jul-2012
https://dl.acm.org/doi/10.5555/2390524.2390660
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