Abstract
As people become elderly, they often suffer from memory loss. This can present itself in a conversation where the participants cannot recall, for example, the name of a place they visited or a person they met. In this work, we present a support system for people in such situations that 1) recognizes utterances that trigger situations where information recall is required and then 2) retrieves the necessary information from data sources such as news articles. Our system is composed of two modules: a recognizer for utterances that trigger information recall and a search engine to retrieve necessary information. We describe the task of detecting utterances that trigger information recall and present a novel corpus. Then, we present the details of the search engine. The performances of baseline models for detecting utterances that trigger information recall suggest that these models achieve excellent performances in terms of recall but there is still have room for improvement in terms of precision. Moreover, the performance of the overall proposed system suggests that the retrieval task is somewhat difficult and merits further investigation.
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
Chang, C.C., Fu, M.H., Huang, H.H., Chen, H.H.: An interactive approach to integrating external textual knowledge for multimodal lifelog retrieval. In: Proceedings of the ACM Workshop on Lifelog Search Challenge, pp. 41–44. ACM (2019)
Chen, H.H., Okumura, M.: Information recall support for elderly people in hyper aged societies. In: Proceedings of the The Web Conference, pp. 431–432 (2018)
Craik, F.I., Byrd, M., Swanson, J.M.: Patterns of memory loss in three elderly samples. Psychol. Aging 2(1), 79 (1987)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008)
Hakkani-Tur, D., et al.: Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM. In: Proceedings of Interspeech (2016)
Higashinaka, R., Funakoshi, K., Inaba, M., Tsunomori, Y., Takahashi, T., Kaji, N.: Overview of dialogue breakdown detection challenge 3, vol. 6 (2017)
Ishigaki, T., Okumura, M.: Detecting utterances eliciting information recall. In: Proceedings of the 12th Edition of its Language Resources and Evaluation Conference (LREC2020) (2020, under review)
Ishigaki, T., Takamura, H., Okumura, M.: Summarizing lengthy questions. In: Proceedings of The 8th International Joint Conference on Natural Language Processing (IJCNLP2017), vol. 1, pp. 792–800 (2017)
Kato, T., Nagai, A., Noda, N., Sumitomo, R., Wu, J., Yamamoto, S.: Utterance intent classification of a spoken dialogue system with efficiently untied recursive autoencoders. In: Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue (SIGDIAL2017), pp. 60–64 (2017)
McCallum, A., Nigam, K., Rennie, J., Seymore, K.: A machine learning approach to building domain-specific search engines. In: Sixteenth International Joint Conference on Artificial Intelligence (IJCAI99), vol. 99, pp. 662–667 (1999)
Olney, A., Louwerse, M., Matthews, E., Marineau, J., Hite-Mitchell, H., Graesser, A.: Utterance classification in autotutor. In: Proceedings of the HLT-NAACL 03 Workshop on Building Educational Applications Using Natural Language Processing, pp. 1–8 (2003)
Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: The 2014 Conference of Empirical Methods in Natural Language Processing (EMNLP2014), pp. 1532–1543 (2014)
Platzer, C., Dustdar, S.: A vector space search engine for web services. In: Third European Conference on Web Services (ECOWS 2005), pp. 9–16 (2005)
Raheja, V., Tetreault, J.: Dialogue act classification with context-aware self-attention. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL2019), pp. 3727–3733. Association for Computational Linguistics, Minneapolis (2019)
Tamura, A., Takamura, H., Okumura, M.: Classification of multiple-sentence questions. In: Dale, R., Wong, K.-F., Su, J., Kwong, O.Y. (eds.) IJCNLP 2005. LNCS (LNAI), vol. 3651, pp. 426–437. Springer, Heidelberg (2005). https://doi.org/10.1007/11562214_38
Uthus, D.C., Aha, D.W.: The ubuntu chat corpus for multiparticipant chat analysis. In: Proceedings of 2013 AAAI Spring Symposium Series (2013)
Acknowledgments
This research was conducted as part of the joint research project “Information Recall Support for Elderly People in Hyper Aged Societies” between the Tokyo Institute of Technology and National Taiwan University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ishigaki, T., You, J., Takimoto, H., Okumura, M. (2020). Supporting Information Recall for Elderly People in Hyper Aged Societies. In: Gao, Q., Zhou, J. (eds) Human Aspects of IT for the Aged Population. Healthy and Active Aging. HCII 2020. Lecture Notes in Computer Science(), vol 12208. Springer, Cham. https://doi.org/10.1007/978-3-030-50249-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-50249-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-50248-5
Online ISBN: 978-3-030-50249-2
eBook Packages: Computer ScienceComputer Science (R0)