Skip to main content

Chatbol, a Chatbot for the Spanish “La Liga”

  • Conference paper
  • First Online:
9th International Workshop on Spoken Dialogue System Technology

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 579))

Abstract

This work describes the development of a social chatbot for the football domain. The chatbot, named chatbol, aims at answering a wide variety of questions related to the Spanish football league “La Liga”. Chatbol is deployed as a Slack client for text-based input interaction with users. One of the main Chatbol’s components, a NLU block, is trained to extract the intents and associated entities related to user’s questions about football players, teams, trainers and fixtures. The information for the entities is obtained by making sparql queries to Wikidata site in real time. Then, the retrieved data is used to update the specific chatbot responses. As a fallback strategy, a retrieval-based conversational engine is incorporated to the chatbot system. It allows for a wider variety and freedom of responses, still football oriented, for the case when the NLU module was unable to reply with high confidence to the user. The retrieval-based response database is composed of real conversations collected both from a IRC football channel and from football-related excerpts picked up across movie captions, extracted from the OpenSubtitles database.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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

Institutional subscriptions

Notes

  1. 1.

    https://medium.com/conversational-interfaces/conversational-interfaces-the-next-big-technological-revolution-4efea1d97606.

  2. 2.

    https://toni.football/.

  3. 3.

    https://botlist.co/bots/tokabot-football.

  4. 4.

    https://www.mediawiki.org/wiki/API.

  5. 5.

    https://api.slack.com/.

  6. 6.

    https://futbolportv.com/.

  7. 7.

    https://chathispano.com/.

  8. 8.

    http://workshop.colips.org/wochat/main_sharedtask.html.

References

  1. Adrian I, Jean V (2016) Moocbuddy: a chatbot for personalized learning with moocs, vol 91

    Google Scholar 

  2. Augello A, Saccone G, Gaglio S, Pilato G (2008) Humorist bot: Bringing computational humour in a chat-bot system. In: International conference on complex, intelligent and software intensive systems, 2008. CISIS 2008. IEEE, pp 703–708

    Google Scholar 

  3. Banchs RE, Li H (2012) IRIS: a chat-oriented dialogue system based on the vector space model. In: The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the System Demonstrations, July 10, 2012, Jeju Island, Korea, pp 37–42

    Google Scholar 

  4. Bocklisch T, Faulker J, Pawlowski N, Nichol A (2017) Rasa: open source language understanding and dialogue management. arXiv preprint arXiv:1712.05181

  5. Brixey J, Hoegen R, Lan W, Rusow J, Singla K, Yin X, Artstein R, Leuski A (2017) Shihbot: a facebook chatbot for sexual health information on hiv/aids. In: Proceedings of the 18th annual SIGdial meeting on discourse and dialogue, pp 370–373

    Google Scholar 

  6. Chandar P, Khazaeni Y, Davis M, Muller M, Crasso M, Liao QV, Shami NS, Geyer W (2017) Leveraging conversational systems to assists new hires during onboarding. In: Bernhaupt R, Dalvi G, Joshi A, K. Balkrishan D, O’Neill J, Winckler M (eds) Human-Computer Interaction - INTERACT 2017. Springer International Publishing, Cham, pp 381–391

    Chapter  Google Scholar 

  7. Fatemi M, Asri LE, Schulz H, He J, Suleman K (2016) Policy networks with two-stage training for dialogue systems. In: Proceedings of the 17th annual meeting of the special interest group on discourse and dialogue, Los Angeles. Association for Computational Linguistics, pp 101–110

    Google Scholar 

  8. Fernández S, Zakhlestin A (2017) Sparql endpoint interface to python. https://github.com/RDFLib/sparqlwrapper

  9. Gašić M, Jurčíček F, Keizer S, Mairesse F, Thomson B, Yu K, Young S (2010) Gaussian processes for fast policy optimisation of pomdp-based dialogue managers. In: Proceedings of the 11th annual meeting of the special interest group on discourse and dialogue, SIGDIAL ’10, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics, pp 201–204

    Google Scholar 

  10. Gonzalez J, Rodrigues P, Cohen A (2017) Fuzzywuzzy: Fuzzy string matching in python. https://github.com/seatgeek/fuzzywuzzy

  11. Honnibal M, Montani I (2017) spacy 2: natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. https://github.com/explosion/spaCy

  12. Jena G, Vashisht M, Basu A, Ungar L, Sedoc J (2017) Enterprise to computer: star trek chatbot. arXiv preprint arXiv:1708.00818

  13. Korobov M (2017) sklearn-crfsuite. https://github.com/TeamHG-Memex/sklearn-crfsuite

  14. Ni L, Lu C, Liu N, Liu J (2017) Mandy: towards a smart primary care chatbot application. In: Chen J, Theeramunkong T, Supnithi T, Tang X (eds) Knowledge and systems sciences. Springer, Singapore, pp 38–52

    Google Scholar 

  15. Oh K-J, Lee D, Ko B, Choi H-J (2017) A chatbot for psychiatric counseling in mental healthcare service based on emotional dialogue analysis and sentence generation. In: 2017 18th IEEE international conference on mobile data management (MDM). IEEE, pp 371–375

    Google Scholar 

  16. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res, 12:2825–2830

    Google Scholar 

  17. Pennington J, Socher R, Manning C (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

    Google Scholar 

  18. Ramesh K, Ravishankaran S, Joshi A, Chandrasekaran K (2017) A survey of design techniques for conversational agents. Springer, Singapore, pp 336–350

    Google Scholar 

  19. Ritter A, Cherry C, Dolan WB (2011) Data-driven response generation in social media. In: Proceedings of the conference on empirical methods in natural language processing, EMNLP ’11, Stroudsburg, PA, USA. Association for Computational Linguistics, pp 583–593

    Google Scholar 

  20. Salton G, Wong A, Yang CS (1975) A vector space model for automatic indexing. Commun ACM 18(11):613–620

    Article  Google Scholar 

  21. Song D, Oh EY, Rice M (2017) Interacting with a conversational agent system for educational purposes in online courses. In: 2017 10th International conference on human system interactions (HSI), pp 78–82

    Google Scholar 

  22. Suglia A, Greco C, Basile P, Semeraro G, Caputo A (2017) An automatic procedure for generating datasets for conversational recommender systems. CLEF

    Google Scholar 

  23. Tiedemann Jörg (2009) News from opus-a collection of multilingual parallel corpora with tools and interfaces. Recent Adv Nat Lang Process 5:237–248

    Article  Google Scholar 

  24. van Rijsbergen CJK (2005) A probabilistic logic for information retrieval. In David EL, Fernández-Luna JM (eds) Advances in information retrieval. Springer, Berlin, Heidelberg, pp 1–6

    Google Scholar 

  25. Vinyals O, Le QV (2015) A neural conversational model. CoRR arXiv:abs/1506.05869

  26. Wallace R (2003) The elements of aiml style

    Google Scholar 

  27. Weizenbaum Joseph (1966) Eliza: a computer program for the study of natural language communica tion between man and machine. Commun ACM 9(1):36–45

    Article  Google Scholar 

  28. Wu Y, Wu W, Li Z, Zhou M (2016) Topic augmented neural network for short text conversation. CoRR arXiv:abs/1605.00090

  29. Xu A, Liu Z, Guo Y, Sinha V, Akkiraju R (2017) A new chatbot for customer service on social media. In: Proceedings of the 2017 CHI conference on human factors in computing systems. ACM, pp 3506–3510

    Google Scholar 

Download references

Acknowledgements

This study has been funded by the Spanish Ministerio de Economía y Competitividad, the European Regional Development Fund and the Agencia Estatal de Investigación, through the postdoctoral senior grant Ramón y Cajal, the contract TEC2015-69266-P (MINECO/FEDER,EU) and the contract PCIN-2017-079 (AEI/MINECO).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos Segura .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Segura, C., Palau, À., Luque, J., Costa-Jussà, M.R., Banchs, R.E. (2019). Chatbol, a Chatbot for the Spanish “La Liga”. In: D'Haro, L., Banchs, R., Li, H. (eds) 9th International Workshop on Spoken Dialogue System Technology. Lecture Notes in Electrical Engineering, vol 579. Springer, Singapore. https://doi.org/10.1007/978-981-13-9443-0_28

Download citation

Publish with us

Policies and ethics