Abstract
In this paper, we describe our work within the research project “CHIM - Chatbot in the Museum”. CHIM is an AI-based chatbot prototype that enables conversational interaction using text and speech input: visitors can ask questions about certain artworks and receive answers in multimodal formats (text, audio, image, video). The application will be tested in the Städel Museum, Frankfurt/Main, Germany. To develop a proper Natural Language Understanding module, we adapted an existing categorization approach, gathered visitor questions, and structured them into twelve distinct content types. The preliminary results suggest that our approach to subdivide the previously overloaded content type meaning into further categories was successful, leading to a more balanced distribution of the data. We further describe the Natural Language Processing mechanisms employed here; these follow a multi-tiered approach using techniques like Rasa, BERT, and cosine-similarity to generate answers with different degrees of effort. Future steps are the implementation of dialog management, the refinement of the NLP strategies by integrating additional answers for selected exhibits, and the implementation of the final layout and interaction design. We are planning to test and evaluate the CHIM prototype on site in the Städel Museum in late 2021.
We want to thank the Städel Museum Frankfurt for their support. This research is part of the CHIM project of the research initiative “KMU-innovativ: Mensch-Technik-Interaktion", which is funded by the Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany under funding number 16SV8331.
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References
The field museum. https://www.fieldmuseum.org/exhibitions/maximo-titanosaur?chat=open. Accessed 30 July 2021
Jüdisches museum berlin. https://www.jmberlin.de/whatsapp-guide-hey-und-herzlich-willkommen. Accessed 30 July 2021
Kunsthalle karlsruhe: Art of chit-chatting. https://www.moodfor.art/chit-chatting. Accessed 02 Nov 2021
Ping! die museumsapp. https://www.museum4punkt0.de/ergebnis/ping-die-museumsapp-spielerisch-durchs-museum. Accessed 02 Nov 2021
Zentrum für kunst und medien. https://zkm.de/de/talk-to-me-chatbots-in-museen. Accessed 30 July 2021
Barth, F., Candello, H., Cavalin, P., Pinhanez, C.: Intentions, meanings, and whys: designing content for voice-based conversational museum guides. In: Proceedings of the 2nd Conference on Conversational User Interfaces, pp. 1–8 (2020)
Bocklisch, T., Faulkner, J., Pawlowski, N., Nichol, A.: Rasa: open source language understanding and dialogue management. arXiv preprint arXiv:1712.05181 (2017)
Ciecko, B.: Examining the impact of artificial intelligence in museums, February 2017
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. CoRR abs/1810.04805 (2018). http://arxiv.org/abs/1810.04805
Falk, J., Dierking, L.: Learning from museums: visitor experiences and the making of meaning, January 2000
Gaia, G., Boiano, S., Borda, A.: Engaging museum visitors with AI: the case of chatbots. In: Giannini, T., Bowen, J.P. (eds.) Museums and Digital Culture. SSCC, pp. 309–329. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-97457-6_15
Huang, A.: Similarity measures for text document clustering. In: Proceedings of the Sixth New Zealand Computer Science Research Student Conference (NZCSRSC 2008), Christchurch, New Zealand, vol. 4, pp. 9–56 (2008)
Kohle, H.: Digitale Bildwissenschaft. Hülsbusch, Glückstadt (2013). http://nbn-resolving.de/urn/resolver.pl?urn=nbn:de:bvb:19-epub-25747-3
Zaman, M.M.U., Schaffer, S., Scheffler, T.: Comparing BERT with an intent based question answering setup for open-ended questions in the museum domain. In: 32. Konferenz Elektronische Sprachsignalverarbeitung. Elektronische Sprachsignalverarbeitung. Elektronische Sprachsignalverarbeitung (ESSV-2021). TUDpress, Dresden (2021)
Zaman, M.M.U., Schaffer, S., Scheffler, T.: Factoid and open-ended question answering with BERT in the museum domain. In: Proceedings of the Conference on Digital Curation Technologies. Conference on Digital Curation Technologies (QURATOR-2021). CEUR Workshop Proceedings (2021)
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Schaffer, S., Ruß, A., Sasse, M.L., Schubotz, L., Gustke, O. (2022). Questions and Answers: Important Steps to Let AI Chatbots Answer Questions in the Museum. In: Wölfel, M., Bernhardt, J., Thiel, S. (eds) ArtsIT, Interactivity and Game Creation. ArtsIT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 422. Springer, Cham. https://doi.org/10.1007/978-3-030-95531-1_24
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