Empowering Air Travelers: A Chatbot for Canadian Air Passenger Rights
Not on the map. Why?
Abstract (arXiv)
The Canadian air travel sector has seen a significant increase in flight delays, cancellations, and other issues concerning passenger rights. Recognizing this demand, we present a chatbot to assist passengers and educate them about their rights. Our system breaks a complex user input into simple queries which are used to retrieve information from a collection of documents detailing air travel regulations. The most relevant passages from these documents are presented along with links to the original documents and the generated queries, enabling users to dissect and leverage the information for their unique circumstances. The system successfully overcomes two predominant challenges: understanding complex user inputs, and delivering accurate answers, free of hallucinations, that passengers can rely on for making informed decisions. A user study comparing the chatbot to a Google search demonstrated the chatbot's usefulness and ease of use. Beyond the primary goal of providing accurate and timely information to air passengers regarding their rights, we hope that this system will also enable further research exploring the tradeoff between the user-friendly conversational interface of chatbots and the accuracy of retrieval systems.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 19 Mar 2024.
Builds on
17 of 36 references.
References · 17
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
- 2018MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modellingcited by 766
- 2018QuAC : Question Answering in Contextcited by 381
- 2020A Survey of Knowledge-Enhanced Text Generationcited by 120
- 2019Multi-hop Reading Comprehension through Question Decomposition and Rescoringcited by 119
- 2020Unsupervised Question Decomposition for Question Answeringcited by 118
- 2018A Dataset for Document Grounded Conversationscited by 104
- 2021Decontextualization: Making Sentences Stand-Alonecited by 102
- 2020doc2dial: A Goal-Oriented Document-Grounded Dialogue Datasetcited by 88
- 2020Sequential Latent Knowledge Selection for Knowledge-Grounded Dialoguecited by 82
- 2019Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documentscited by 57
- 2021MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documentscited by 53
5 more
- 2018FlowQA: Grasping Flow in History for Conversational Machine Comprehensioncited by 33
- 2023Generative Knowledge Selection for Knowledge-Grounded Dialoguescited by 15
- 2023JusticeBot: A Methodology for Building Augmented Intelligence Tools for Laypeople to Increase Access to Justicecited by 9
- 2023textitDial BeInfo for Faithfulness: Improving Factuality of Information-Seeking Dialogue via Behavioural Fine-Tuningcited by 9
- 2023Evaluating Large Language Models for Document-grounded Response Generation in Information-Seeking Dialoguescited by 5
Built on by
Citing papers per year: 2025: 2; 2026: 2.
4 papers.
- 2025SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Modelscited by 15
- 2025Attention to Non-Adopterscited by 4
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agentscited by 1
- 2026Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversationcited by 1
- 2026Towards Proactive Information Probing: Customer Service Chatbots Harvesting Value from Conversation13 Apr 2026
- 2026A Concise Agent is Less Expert: Revealing Side Effects of Using Style Features on Conversational Agents15 Jan 2026
- 2025Attention to Non-Adopters10 Oct 2025
- 2025SimMark: A Robust Sentence-Level Similarity-Based Watermarking Algorithm for Large Language Models5 Feb 2025