Automatic Conversation Turn-Taking Segmentation in Semantic Facet
- Authors
- Jung, D.; Cho, Yoon Sik
- Issue Date
- 2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Live Text Stream; Token Classification; Turn-taking Segmentation
- Citation
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- Journal Title
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/69978
- DOI
- 10.1109/ICEIC57457.2023.10049858
- ISSN
- 0000-0000
- Abstract
- Turn-taking is a significant aspect of a smooth conversation system. Detecting end-of-turn can be difficult for automatic conversation systems, and this can cause misleading conversation systems. To make a conversational system recognizing turn transition points, we propose a token-level turn-taking segmentation using linguistic features. This task imitates the automatic speech recognition environment by organizing several settings. Moreover, we utilize GPT-2, which is well known as a pretrained generative language model, to be able to predict in token-level live text stream. We evaluate our model compared to RNN series models in general conversation datasets and explore model prediction with test sample scenarios. © 2023 IEEE.
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