DoQA -- Accessing Domain-Specific FAQs via Conversational QA
The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA pairs. The dialogues are collected from three Stack Exchange sites using the Wizard of Oz method with crowdsourcing. Compared to previous work, DoQA comprises well-defined information needs, leading to more coherent and natural conversations with less factoid questions and is multi-domain. In addition, we introduce a more realistic information retrieval(IR) scenario where the system needs to find the answer in any of the FAQ documents. The results of an existing, strong, system show that, thanks to transfer learning from a Wikipedia QA dataset and fine tuning on a single FAQ domain, it is possible to build high quality conversational QA systems for FAQs without in-domain training data. The good results carry over into the more challenging IR scenario. In both cases, there is still ample room for improvement, as indicated by the higher human upperbound.
Code (0)
등록된 구현이 없습니다.
Tasks
Conversational Question AnsweringInformation RetrievalQuestion AnsweringRetrievalTransfer LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DoQA - Accessing Domain-Specific FAQs via Conversational QA
The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites. We present DoQA, a dataset with 2,437 dialogues and 10,917 QA …
Conversational Question AnsweringInformation RetrievalQuestion AnsweringRetrieval+1Improving Conversational Question Answering Systems after Deployment using Feedback-Weighted Learning
The interaction of conversational systems with users poses an exciting opportunity for improving them after deployment, but little evidence has been provided of its feasibility. In most applications, users are not able t…
Conversational Question AnsweringDocument ClassificationQuestion AnsweringFAQ-Gen: An automated system to generate domain-specific FAQs to aid content comprehension
Frequently Asked Questions (FAQs) refer to the most common inquiries about specific content. They serve as content comprehension aids by simplifying topics and enhancing understanding through succinct presentation of inf…
Question AnsweringorgFAQ: A New Dataset and Analysis on Organizational FAQs and User Questions
Frequently Asked Questions (FAQ) webpages are created by organizations for their users. FAQs are used in several scenarios, e.g., to answer user questions. On the other hand, the content of FAQs is affected by user quest…
Multi-turn Dialog System on Single-turn Data in Medical Domain
Recently there has been a huge interest in dialog systems. This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain. This r…