Research on tourism cross-language intention recognition method based on XLM-R

Hao Zhu; Altenbek Gulila; Fangri Ren; Han Liu

doi:10.1117/12.3011446

8 November 2023 Research on tourism cross-language intention recognition method based on XLM-R

Hao Zhu, Altenbek Gulila, Fangri Ren, Han Liu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 1292308 (2023) https://doi.org/10.1117/12.3011446
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

Intention recognition task is an important subtask in intelligent question answering research. At present, the task of intention recognition is usually oriented to a single language, and there are relatively few studies on cross-language intention recognition. In this paper, the "XLM-R+TextCNN" model is proposed for cross-language intention recognition task. The text vector of multi-language representation is generated by XLM-R pre-training model, and text features are obtained by TextCNN model to realize cross-language intention recognition. The experimental data set is the self-built tourism data set. The experimental results show that the model has achieved good results in a number of comparative evaluation, which verifies the feasibility and effectiveness of the proposed method.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Zhu, Altenbek Gulila, Fangri Ren, and Han Liu "Research on tourism cross-language intention recognition method based on XLM-R", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 1292308 (8 November 2023); https://doi.org/10.1117/12.3011446

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KEYWORDS

Convolution

Feature extraction

Education and training

Performance modeling

Data modeling

Deep learning

Transformers

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