Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Human Computer Interaction

IHCI 2019: Intelligent Human Computer Interaction pp 124–137Cite as

RNN Based Language Generation Models for a Hindi Dialogue System

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11886))

Abstract

Natural Language Generation (NLG) is a crucial component of a Spoken Dialogue System. Its task is to generate utterances with intended attributes like fluency, variation, readability, scalability and adequacy. As the handcrafted models are rigid and tedious to build, people have proposed many statistical and deep-learning based models to bring about more suitable options for generating utterance on a given Dialogue-Act (DA). This paper presents some Recurrent Neural Network Language Generation (RNNLG) framework based models along with their analysis of how they extract intended meaning in terms of content planning (modelling semantic input) and surface realization (final sentence generation) on a proposed unaligned Hindi dataset. The models have shown consistent performance on our natively developed dataset where the Modified-Semantically-Controlled LSTM (MSC-LSTM) performs better than all in terms of total slot-error (T-Error).

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Notes

  1. 1.

    https://github.com/shawnwun/RNNLG.

  2. 2.

    DA-vector is a 1-hot encoded vector of action-type and slot-value-type where values are corresponding to occurrences of a given slot e.g. sv.name._1, sv.name._2.

  3. 3.

    Here, token is used to represent both word and slot-token e.g. SLOT_NAME, SLOT_AREA etc. in a delexicalised sentence.

  4. 4.

    https://radimrehurek.com/gensim/index.html.

  5. 5.

    The Cambridge University Python Multi-domain Statistical Dialogue System Toolkit http://www.camdial.org/pydial/.

  6. 6.

    Utterances having minimum slot-error (S-Error) are selected.

  7. 7.

    Ex.: inform(name= ;pricerange= ;kidsallowed=yes;food= ).

References

  1. Walker, M.A., Rambow, O.C., Rogati, M.: Training a sentence planner for spoken dialogue using boosting. Comput. Speech Lang. 16(3), 409–433 (2002). Spoken Language Generation

    Article  Google Scholar 

  2. Stent, A., Prasad, R., Walker, M.: Trainable sentence planning for complex information presentation in spoken dialog systems. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, ACL 2004. Association for Computational Linguistics, Stroudsburg, PA, USA (2004)

    Google Scholar 

  3. Malviya, S., Tiwary, U.S.: Knowledge based summarization and document generation using bayesian network. Procedia Comput. Sci. 89, 333–340 (2016)

    Article  Google Scholar 

  4. Langkilde, I., Knight, K.: Generation that exploits corpus-based statistical knowledge. In: Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics, ACL 1998/COLING 1998, Stroudsburg, PA, USA, pp. 704–710 (1998)

    Google Scholar 

  5. Rieser, V., Lemon, O.: Natural language generation as planning under uncertainty for spoken dialogue systems. In: Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pp. 683–691. Association for Computational Linguistics, Athens, Greece, March 2009

    Google Scholar 

  6. Oh, A.H., Rudnicky, A.I.: Stochastic language generation for spoken dialogue systems. In: ANLP-NAACL 2000 Workshop: Conversational Systems (2000)

    Google Scholar 

  7. Mairesse, F., et al.: Phrase-based statistical language generation using graphical models and active learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 1552–1561 (2010)

    Google Scholar 

  8. Mairesse, F., Young, S.: Stochastic language generation in dialogue using factored language models. Comput. Linguist. 40(4), 763–799 (2014)

    Article  Google Scholar 

  9. Konstas, I., Lapata, M.: A global model for concept-to-text generation. J. Artif. Intell. Res. 48, 305–346 (2013)

    Article  Google Scholar 

  10. Dušek, O., Jurčíček, F.: Sequence-to-sequence generation for spoken dialogue via deep syntax trees and strings. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). pp. 45–51. Berlin, Germany (Aug 2016)

    Google Scholar 

  11. Mei, H., Bansal, M., Walter, M.R.: What to talk about and how? selective generation using LSTMs with coarse-to-fine alignment. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics, San Diego, California, pp. 720–730 (2016)

    Google Scholar 

  12. Wen, T.H., Gašić, M., Mrkšić, N., Su, P.H., Vandyke, D., Young, S.: Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 1711–1721, September 2015

    Google Scholar 

  13. Wen, T.H., Young, S.: Recurrent neural network language generation for spoken dialogue systems. Comput. Speech Lang. 63, 101017 (2020)

    Google Scholar 

  14. Jain, S., Malviya, S., Mishra, R., Tiwary, U.S.: Sentiment analysis: An empirical comparative study of various machine learning approaches. In: Proceedings of the 14th International Conference on Natural Language Processing (ICON-2017), pp. 112–121. NLP Association of India, Kolkata, India, December 2017

    Google Scholar 

  15. Jurčíček, F., Dušek, O., Plátek, O., Žilka, L.: Alex: a statistical dialogue systems framework. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2014. LNCS (LNAI), vol. 8655, pp. 587–594. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10816-2_71

    Chapter  Google Scholar 

  16. Hu, Z., Halberg, G., Jimenez, C.R., Walker, M.A.: Entrainment in pedestrian direction giving: how many kinds of entrainment? In: Rudnicky, A., Raux, A., Lane, I., Misu, T. (eds.) Situated Dialog in Speech-Based Human-Computer Interaction. SCT, pp. 151–164. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-21834-2_14

    Chapter  Google Scholar 

  17. Mikolov, T., Karafiát, M., Burget, L., Černocký, J., Khudanpur, S.: Recurrent neural network based language model. In: Eleventh Annual Conference of the International Speech Communication Association (2010)

    Google Scholar 

  18. Dhariya, O., Malviya, S., Tiwary, U.S.: A hybrid approach for hindi-english machine translation. In: 2017 International Conference on Information Networking (ICOIN), pp. 389–394. IEEE (2017)

    Google Scholar 

  19. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Proceedings of the ICLR (2015)

    Google Scholar 

  20. Wu, Y., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016)

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology Allahabad, Allahabad, India

    Sumit Singh, Shrikant Malviya, Rohit Mishra, Santosh Kumar Barnwal & Uma Shanker Tiwary

Authors
  1. Sumit Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shrikant Malviya
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rohit Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santosh Kumar Barnwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Uma Shanker Tiwary
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shrikant Malviya .

Editor information

Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  2. Indian Institute of Technology, Jodhpur, India

    Santanu Chaudhury

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, S., Malviya, S., Mishra, R., Barnwal, S.K., Tiwary, U.S. (2020). RNN Based Language Generation Models for a Hindi Dialogue System. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-44689-5_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44688-8

  • Online ISBN: 978-3-030-44689-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation