Abstract
Explainable Artificial Intelligence (AI) based chatbot is one of the most ambitious and unsolved sectors of conversational AI. Recently, there has been a boom in the neural network architecture such as BERT and GPTs that understand the sense of such words/phrases in the sentence. However, such models fail to explain the logical reasoning behind the language understanding thereby making the base of chatbot unreliable. In this paper, we design and extend the previous TM based Word Sense Disambiguation task on complete 20 words as well as design a fully explainable word sense classifier using the Tsetlin Machine (TM) that supports the chatbot to understand the concept of the word/phrases. Our experiments show that the proposed model performs on par with the state-of-the-art accuracy on the publicly available CoarseWSD-balanced dataset. In addition, we explore in-depth how each interpretable clause of TM carries context information that can be easily explained by a human for designing a trustful chatbot.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Why chatbots fail: Limitations of chatbots. https://medium.com/voice-tec
Abeyrathna, K.D., Granmo, O.C., Zhang, X., Jiao, L., Goodwin, M.: The regression Tsetlin machine: a novel approach to interpretable nonlinear regression. Philos. Trans. R. Soc. A: Math. Phys. Eng. Sci. 378 (2019)
Agirre, E., Edmonds, P.: Word Sense Disambiguation: Algorithms and Applications. Text, Speech and Language Technology, Springer, Heidelberg (2006). https://doi.org/10.1007/978-1-4020-4809-8
Berge, G.T., Granmo, O., Tveit, T.O., Goodwin, M., Jiao, L., Matheussen, B.V.: Using the Tsetlin machine to learn human-interpretable rules for high-accuracy text categorization with medical applications. IEEE Access 7, 115134–115146 (2019). https://doi.org/10.1109/ACCESS.2019.2935416
Bhattarai, B., Granmo, O.C., Jiao, L.: Measuring the novelty of natural language text using the conjunctive clauses of a Tsetlin machine text classifier. arXiv abs/2011.08755 (2020)
Bhattarai, B., Granmo, O.C., Jiao, L.: Word-level human interpretable scoring mechanism for novel text detection using Tsetlin machines (2021)
Buhrmester, V., Münch, D., Arens, M.: Analysis of explainers of black box deep neural networks for computer vision: a survey (2019)
Cem, D.: 8 epic chatbot/conversational bot failures (2020). https://research.aimultiple.com/chatbot-fail/
Dongsuk, O., Kwon, S., Kim, K., Ko, Y.: Word sense disambiguation based on word similarity calculation using word vector representation from a knowledge-based graph. In: COLING (2018)
Glass, A., McGuinness, D.L., Wolverton, M.: Toward establishing trust in adaptive agents. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, IUI 2008, pp. 227–236. Association for Computing Machinery, New York (2008)
Granmo, O.C.: The Tsetlin machine - a game theoretic bandit driven approach to optimal pattern recognition with propositional logic (2018)
Granmo, O.C., Glimsdal, S., Jiao, L., Goodwin, M., Omlin, C.W., Berge, G.T.: The convolutional Tsetlin machine (2019)
Jiao, L., Zhang, X., Granmo, O.C., Abeyrathna, K.D.: On the convergence of Tsetlin machines for the XOR operator. arXiv preprint arXiv:2101.02547 (2021)
Kågebäck, M., Salomonsson, H.: Word sense disambiguation using a bidirectional LSTM. In: CogALex@COLING (2016)
Khattak, F.K., Jeblee, S., Pou-Prom, C., Abdalla, M., Meaney, C., Rudzicz, F.: A survey of word embeddings for clinical text. J. Biomed. Inform.: X 4, 100057 (2019)
Liao, K., Ye, D., Xi, Y.: Research on enterprise text knowledge classification based on knowledge schema. In: 2010 2nd IEEE International Conference on Information Management and Engineering, pp. 452–456 (2010). https://doi.org/10.1109/ICIME.2010.5477609
Loureiro, D., Rezaee, K., Pilehvar, M.T., Camacho-Collados, J.: Analysis and evaluation of language models for word sense disambiguation. Comput. Linguist. 47, 387–443 (2021)
Luger, E., Sellen, A.: “Like having a really bad PA”: the gulf between user expectation and experience of conversational agents. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, CHI 2016, pp. 5286–5297. Association for Computing Machinery, New York (2016)
Meyer, R.: Even early focus groups hated clippy, June. https://www.theatlantic.com/
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. arXiv abs/1310.4546 (2013)
Navigli, R., Velardi, P.: Structural semantic interconnection: a knowledge-based approach to word sense disambiguation. In: SENSEVAL@ACL (2004)
Porcheron, M., Fischer, J.E., Reeves, S., Sharples, S.: Voice interfaces in everyday life. In: Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, CHI 2018, pp. 1–12. Association for Computing Machinery, New York (2018)
Radlinski, F., Craswell, N.: A theoretical framework for conversational search. In: Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval, CHIIR 2017, pp. 117–126. Association for Computing Machinery, New York (2017)
Raganato, A., Camacho-Collados, J., Navigli, R.: Word sense disambiguation: a unified evaluation framework and empirical comparison. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, Valencia, Spain: Volume 1, Long Papers, pp. 99–110 (2017)
Rezaeinia, S.M., Rahmani, R., Ghodsi, A., Veisi, H.: Sentiment analysis based on improved pre-trained word embeddings. Expert Syst. Appl. 117, 139–147 (2019)
Rudin, C.: Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead (2018)
Sadi, M.F., Ansari, E., Afsharchi, M.: Supervised word sense disambiguation using new features based on word embeddings. J. Intell. Fuzzy Syst. 37, 1467–1476 (2019)
Saha, R., Granmo, O.-C., Goodwin, M.: Mining interpretable rules for sentiment and semantic relation analysis using Tsetlin machines. In: Bramer, M., Ellis, R. (eds.) SGAI 2020. LNCS (LNAI), vol. 12498, pp. 67–78. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63799-6_5
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Wang, Y., et al.: Clinical information extraction applications: a literature review. J. Biomed. Inform. 77, 34–49 (2018). https://doi.org/10.1016/j.jbi.2017.11.011. http://www.sciencedirect.com/science/article/pii/S1532046417302563
Xiao, J., Stasko, J., Catrambone, R.: An empirical study of the effect of agent competence on user performance and perception. In: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS 2004, vol. 1, pp. 178–185. IEEE Computer Society, USA (2004)
Xu, W.: Toward human-centered AI: a perspective from human-computer interaction. Interactions 26(4), 42–46 (2019)
Yadav, R.K., Jiao, L., Granmo, O.C., Goodwin, M.: Human-level interpretable learning for aspect-based sentiment analysis. In: The Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021). AAAI (2021)
Yadav, R.K., Jiao, L., Granmo, O., Goodwin, M.: Interpretability in word sense disambiguation using Tsetlin machine. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, Volume 2: ICAART, pp. 402–409. INSTICC, SciTePress (2021)
Yuan, D., Richardson, J., Doherty, R., Evans, C., Altendorf, E.: Semi-supervised word sense disambiguation with neural models. In: COLING (2016)
Zhang, X., Jiao, L., Granmo, O.C., Goodwin, M.: On the convergence of Tsetlin machines for the IDENTITY- and NOT operators. IEEE Trans. Pattern Anal. Mach. Intell. (2021, accepted)
Zhong, Z., Ng, H.T.: It makes sense: a wide-coverage word sense disambiguation system for free text. In: ACL (2010)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Yadav, R.K., Jiao, L., Granmo, OC., Goodwin, M. (2022). An Interpretable Word Sense Classifier for Human Explainable Chatbot. In: Rocha, A.P., Steels, L., van den Herik, J. (eds) Agents and Artificial Intelligence. ICAART 2021. Lecture Notes in Computer Science(), vol 13251. Springer, Cham. https://doi.org/10.1007/978-3-031-10161-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-10161-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-10160-1
Online ISBN: 978-3-031-10161-8
eBook Packages: Computer ScienceComputer Science (R0)