TY - GEN
T1 - ATCBERT
T2 - 26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
AU - Hui, Yi
AU - Cai, Kaiquan
AU - Zhang, Minghua
AU - Qian, Shengsheng
AU - Yang, Yang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Intent recognition in Air Traffic Control (ATC) aims to identify the pilot-controller vocal communication intents, which play a critical role in maintaining the safety and efficiency of air traffic. However, the recognition performance of intents with few samples is limited by the number of corresponding instructions in the scenario of an AI-assisted ATC operation. In this paper, ATCBERT is purposed as a novel framework for few-shot intent recognition to achieve high recognition accuracy for some intents in air traffic. Firstly, a joint pre-trained module is designed. By using the large corpus data labeled in the industry, Bidirectional Encoder Representation from Transformers (BERT) is fine-tuned to obtain the feature representation of the corpus in the domain of ATC. Besides, the masked language model is introduced to capture contextual information so as to enhance generalization ability. Secondly, the few-shot classification module is proposed. The classifier is trained and updated by a support set selected randomly from a dataset with few samples, and it is used to predict the intent of a new corpus. Finally, the proposed model is validated in the real-world ATC dataset derived from pilot-controller vocal communication in Beijing, China. It achieves a classification accuracy of 93.6% for few-shot intent recognition, outperforming the performance of the baselines. The findings of this research provide insights for intent recognition in ATC.
AB - Intent recognition in Air Traffic Control (ATC) aims to identify the pilot-controller vocal communication intents, which play a critical role in maintaining the safety and efficiency of air traffic. However, the recognition performance of intents with few samples is limited by the number of corresponding instructions in the scenario of an AI-assisted ATC operation. In this paper, ATCBERT is purposed as a novel framework for few-shot intent recognition to achieve high recognition accuracy for some intents in air traffic. Firstly, a joint pre-trained module is designed. By using the large corpus data labeled in the industry, Bidirectional Encoder Representation from Transformers (BERT) is fine-tuned to obtain the feature representation of the corpus in the domain of ATC. Besides, the masked language model is introduced to capture contextual information so as to enhance generalization ability. Secondly, the few-shot classification module is proposed. The classifier is trained and updated by a support set selected randomly from a dataset with few samples, and it is used to predict the intent of a new corpus. Finally, the proposed model is validated in the real-world ATC dataset derived from pilot-controller vocal communication in Beijing, China. It achieves a classification accuracy of 93.6% for few-shot intent recognition, outperforming the performance of the baselines. The findings of this research provide insights for intent recognition in ATC.
KW - air traffic control
KW - few-shot intent recognition
KW - instruction understanding
KW - natural language understanding
KW - pilot-controller vocal communication
UR - https://www.scopus.com/pages/publications/85186511908
U2 - 10.1109/ITSC57777.2023.10421896
DO - 10.1109/ITSC57777.2023.10421896
M3 - 会议稿件
AN - SCOPUS:85186511908
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 4510
EP - 4517
BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 24 September 2023 through 28 September 2023
ER -