Abstract
Aviation communication significantly influences the success of flight operations, ensuring safety of lives and efficient air transportation. In day-to-day flight operations, air traffic controllers (ATCos) would timely communicate instructions to pilots using specific phraseology for aircraft manipulation . However, pilots, originating from diverse backgrounds and understanding of English language, have struggled with conforming to strict phraseology for readback and communication in the live operation, this problem had not been effectively addressed over the past decades. Traditionally, aviation communication training involved expensive setups and resources, often relying on human-in-the-loop (HIL) air traffic simulations that demand allocating a specific environment, domain experts for participation, and substantial amount of annotated data for simulation. Therefore, we would like to propose an NLP-oriented training agent and address these challenges. Our approach involves leveraging only natural language capabilities and fine-tuning on communication data to generate instructions based on input scenarios (keywords). Given the absence of prior references for this business problem, we investigated the feasibility of our proposed solution by 1) generating all instructions at once and 2) generating one instruction while incorporating conversational history in each input. Our findings affirm the feasibility of this approach, highlighting the effectiveness of fine-tuning pre-trained models and large language models in advancing aviation communication training.- Anthology ID:
- 2024.naacl-industry.8
- Volume:
- Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Yi Yang, Aida Davani, Avi Sil, Anoop Kumar
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 89–96
- Language:
- URL:
- https://aclanthology.org/2024.naacl-industry.8
- DOI:
- Cite (ACL):
- Xiaochen Liu, Bowei Zou, and AiTi Aw. 2024. An NLP-Focused Pilot Training Agent for Safe and Efficient Aviation Communication. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 6: Industry Track), pages 89–96, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- An NLP-Focused Pilot Training Agent for Safe and Efficient Aviation Communication (Liu et al., NAACL 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.naacl-industry.8.pdf