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Hierarchical reinforcement learning and hidden Markov models for task-oriented natural language generation

Published: 19 June 2011 Publication History

Abstract

Surface realisation decisions in language generation can be sensitive to a language model, but also to decisions of content selection. We therefore propose the joint optimisation of content selection and surface realisation using Hierarchical Reinforcement Learning (HRL). To this end, we suggest a novel reward function that is induced from human data and is especially suited for surface realisation. It is based on a generation space in the form of a Hidden Markov Model (HMM). Results in terms of task success and human-likeness suggest that our unified approach performs better than greedy or random baselines.

References

[1]
Gabor Angeli, Percy Liang, and Dan Klein. 2010. A simple domain-independent probabilistic approach to generation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP '10, pages 502--512.
[2]
Srinivas Bangalore and Owen Rambow. 2000. Exploiting a probabilistic hierarchical model for generation. In Proceedings of the 18th Conference on Computational Linguistics (ACL) - Volume 1, pages 42--48.
[3]
Regina Barzilay and Lillian Lee. 2002. Bootstrapping lexical choice via multiple-sequence alignment. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 164--171.
[4]
Anja Belz and Ehud Reiter. 2006. Comparing Automatic and Human Evaluation of NLG Systems. In Proc. of the European Chapter of the Association for Computational Linguistics (EACL), pages 313--320.
[5]
Anja Belz. 2008. Automatic generation of weather forecast texts using comprehensive probabilistic generation-space models. Natural Language Engineering, 1:1--26.
[6]
Heriberto Cuay�huitl, Steve Renals, Oliver Lemon, and Hiroshi Shimodaira. 2005. Human-Computer Dialogue Simulation Using Hidden Markov Models. In Proc. of ASRU, pages 290--295.
[7]
Nina Dethlefs and Heriberto Cuay�huitl. 2010. Hierarchical Reinforcement Learning for Adaptive Text Generation. Proceeding of the 6th International Conference on Natural Language Generation (INLG).
[8]
Nina Dethlefs, Heriberto Cuay�huitl, and Jette Viethen. 2011. Optimising Natural Language Generation Decision Making for Situated Dialogue. In Proc. of the 12th Annual SIGdial Meeting on Discourse and Dialogue.
[9]
Thomas G. Dietterich. 1999. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Journal of Artificial Intelligence Research, 13:227--303.
[10]
Mary Ellen Foster and Jon Oberlander. 2006. Data-driven generation of emphatic facial displays. In Proc. of the European Chapter of the Association for Computational Linguistic (EACL), pages 353--360.
[11]
Andrew Gargett, Konstantina Garoufi, Alexander Koller, and Kristina Striegnitz. 2010. The GIVE-2 corpus of giving instructions in virtual environments. In LREC.
[12]
Michael A. K. Halliday and Ruqaiya Hasan. 1976. Cohesion in English. Longman, London.
[13]
Srinivasan Janarthanam and Oliver Lemon. 2010. Learning to adapt to unknown users: referring expression generation in spoken dialogue systems. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 69--78.
[14]
Alexander Koller, Kristina Striegnitz, Donna Byron, Justine Cassell, Robert Dale, Johanna Moore, and Jon Oberlander. 2010. The first challenge on generating instructions in virtual environments. In M. Theune and E. Krahmer, editors, Empirical Methods on Natural Language Generation, pages 337--361, Berlin/Heidelberg, Germany. Springer.
[15]
Irene Langkilde and Kevin Knight. 1998. Generation that exploits corpus-based statistical knowledge. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics (ACL), pages 704--710.
[16]
W J M Levelt and S Kelter. 1982. Surface form and memory in question answering. Cognitive Psychology, 14.
[17]
François Mairesse, Milica Gašić, Filip Jurčíček, Simon Keizer, Blaise Thomson, Kai Yu, and Steve Young. 2010. Phrase-based statistical language generation using graphical models and active learning. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 1552--1561.
[18]
Alice H. Oh and Alexander I. Rudnicky. 2000. Stochastic language generation for spoken dialogue systems. In Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems - Volume 3, pages 27--32.
[19]
Martin J. Pickering and Simon Garrod. 2004. Toward a mechanistc psychology of dialog. Behavioral and Brain Sciences, 27.
[20]
L R Rabiner. 1989. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In Proceedings of IEEE, pages 257--286.
[21]
Verena Rieser, Oliver Lemon, and Xingkun Liu. 2010. Optimising information presentation for spoken dialogue systems. In Proc. of the Annual Meeting of the Association for Computational Lingustics (ACL), pages 1009--1018.
[22]
Richard S Sutton and Andrew G Barto. 1998. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA.
[23]
Menno van Zaanen. 2000. Bootstrapping syntax and recursion using alginment-based learning. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 1063--1070, San Francisco, CA, USA.
[24]
Marilyn A. Walker, Diane J. Litman, Candace A. Kamm, and Alicia Abella. 1997. PARADISE: A framework for evaluating spoken dialogue agents. In Proc. of the Annual Meeting of the Association for Computational Linguistics (ACL), pages 271--280.
[25]
Michael White. 2004. Reining in CCG chart realization. In Proc. of the International Conference on Natural Language Generation (INLG), pages 182--191.

Cited By

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  • (2018)Inferring Extended Probabilistic Finite-State Automaton Models from Software ExecutionsACM Transactions on Software Engineering and Methodology10.1145/319688327:1(1-39)Online publication date: 5-Jun-2018
  • (2015)Test Case Prioritization Using Extended DigraphsACM Transactions on Software Engineering and Methodology10.1145/278920925:1(1-41)Online publication date: 2-Dec-2015
  • (2014)Nonstrict Hierarchical Reinforcement Learning for Interactive Systems and RobotsACM Transactions on Interactive Intelligent Systems10.1145/26590034:3(1-30)Online publication date: 14-Oct-2014
  • Show More Cited By

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cover image DL Hosted proceedings
HLT '11: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
June 2011
765 pages
ISBN:9781932432886

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Association for Computational Linguistics

United States

Publication History

Published: 19 June 2011

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Overall Acceptance Rate 240 of 768 submissions, 31%

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Cited By

View all
  • (2018)Inferring Extended Probabilistic Finite-State Automaton Models from Software ExecutionsACM Transactions on Software Engineering and Methodology10.1145/319688327:1(1-39)Online publication date: 5-Jun-2018
  • (2015)Test Case Prioritization Using Extended DigraphsACM Transactions on Software Engineering and Methodology10.1145/278920925:1(1-41)Online publication date: 2-Dec-2015
  • (2014)Nonstrict Hierarchical Reinforcement Learning for Interactive Systems and RobotsACM Transactions on Interactive Intelligent Systems10.1145/26590034:3(1-30)Online publication date: 14-Oct-2014
  • (2014)Natural language generation as incremental planning under uncertaintyIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASL.2014.231527122:5(979-994)Online publication date: 1-May-2014
  • (2012)Comparing HMMs and Bayesian networks for surface realisationProceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies10.5555/2382029.2382135(636-640)Online publication date: 3-Jun-2012
  • (2011)The Bremen system for the GIVE-2.5 challengeProceedings of the 13th European Workshop on Natural Language Generation10.5555/2187681.2187734(284-289)Online publication date: 28-Sep-2011
  • (2011)The GRUVE challengeProceedings of the 13th European Workshop on Natural Language Generation10.5555/2187681.2187717(208-211)Online publication date: 28-Sep-2011
  • (2011)Adaptive information presentation for spoken dialogue systemsProceedings of the 13th European Workshop on Natural Language Generation10.5555/2187681.2187698(102-109)Online publication date: 28-Sep-2011
  • (2011)Optimising natural language generation decision making for situated dialogueProceedings of the SIGDIAL 2011 Conference10.5555/2132890.2132901(78-87)Online publication date: 17-Jun-2011

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