# A question-answering system for aircraft pilots' documentation

arXiv: https://arxiv.org/abs/2011.13284 · HTML: https://mlsanity.com/paper/2011.13284/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2020.

## Built on by (most cited first, 1 of 1)

- [Knowledge Graph - Deep Learning: A Case Study in Question Answering in Aviation Safety Domain](https://mlsanity.com/paper/2205.15952/) (2022)

## Abstract (arXiv)

The aerospace industry relies on massive collections of complex and technical documents covering system descriptions, manuals or procedures. This paper presents a question answering (QA) system that would help aircraft pilots access information in this documentation by naturally interacting with the system and asking questions in natural language. After describing each module of the dialog system, we present a multi-task based approach for the QA module which enables performance improvement on a Flight Crew Operating Manual (FCOM) dataset. A method to combine scores from the retriever and the QA modules is also presented.