Enhancing Aviation Communication Training with Natural Language Processing (NLP): A Cost-Effective and Efficient Approach
Aviation communication is crucial for safe and efficient flight operations. However, pilots often struggle to adhere to strict phraseology due to diverse cultural backgrounds and varying levels of language proficiency. Traditional training methods involve expensive setups and reliance on human-in-the-loop simulations. To overcome these challenges, this research proposes an NLP-focused training agent that leverages natural language capabilities and fine-tuning communication data to generate instructions based on input scenarios. The results demonstrate the significant efficacy of the proposed NLP-focused training agent in generating personalised instructions for pilots.
Revolutionising Aviation Communication Training with NLP
This research marks the NLP community's first effort to use large language models (LLMs) to construct dialogue systems in aviation, introducing a novel approach to enhance communication skills in the aviation industry. The proposed training agent, capable of generating tailored instructions based on specific input scenarios, represents a significant breakthrough. It addresses individual pilot needs, improves comprehension, and enhances adherence to domain-specific phraseology.
From a technical perspective, the study demonstrates the feasibility of fine-tuning both pre-trained language models and LLMs for aviation communication training, The method enhances explainability by highlighting specific keywords in the input that significantly impact the generated content. Such a feature ensures clarity for non-technical users and provides a transparent understanding of the rationale behind the decision-making processes in generating aviation instructions.
Additionally, the proposed solution introduces a cost-effective training method that eliminates the need for expensive human-in-the-loop simulations and extensive annotated data entries. This innovation makes aviation communication training more accessible and scalable.
The research was awarded the Best Paper Award at the NAACL 2024 Industry Track.
Instruction Generation by PLM and LLM
Leveraging both pre-trained language models (PLMs) and large language models (LLMs) such as GPT-2, BART, and Llama2, the research employs two data templates to ensure coherent generation of instructional content. It uses natural language features and contextual vocabulary to predict the entire sequence of instructions at a specific timestamp or one instruction at a time.
Integration of Existing NLP Methods
To enhance the effectiveness of the training agent, existing NLP methods, including semantic-slot filling and readback error detection, are integrated to validate pilots’ input during training. This integration ensures a robust and comprehensive training experience, supporting the development and implementation of the application front end. The overall workflow is shown in Figure 1.
Figure 1. Workflow of pilot training agent.
Future Work
Future work will involve further exploration to validate and refine the NLP-focused training agent's performance, including: 1) testing the agent in diverse scenarios, 2) evaluating its adaptability to different communication contexts, and 3) measuring its impact on pilot communication proficiency. Additionally, the research will be extended to explore the application of NLP techniques in other areas, such as metro traffic communications audit and maritime communication management.
