Open-Domain Question Answering Goes Conversational via Question Rewriting
AuthorsRaviteja Anantha*, Svitlana Vakulenko*, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
AuthorsRaviteja Anantha*, Svitlana Vakulenko*, Zhucheng Tu, Shayne Longpre, Stephen Pulman, Srinivas Chappidi
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational questions within a collection of 10M web pages (split into 54M passages). Answers to questions in the same conversation may be distributed across several web pages. QReCC provides annotations that allow us to train and evaluate individual subtasks of question rewriting, passage retrieval and reading comprehension required for the end-to-end conversational question answering (QA) task. We report the effectiveness of a strong baseline approach that combines the state-of-the-art model for question rewriting, and competitive models for open-domain QA. Our results set the first baseline for the QReCC dataset with F1 of 19.10, compared to the human upper bound of 75.45, indicating the difficulty of the setup and a large room for improvement.
*Equal Contributions
February 21, 2021research area Knowledge Bases and Search, research area Speech and Natural Language Processingconference WSDM
Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns. We address the conversational QA task by decomposing it into question rewriting and question answering subtasks. The question rewriting (QR) subtask is specifically designed to reformulate ambiguous questions, which depend on the conversational context, into unambiguous questions that can be correctly...
November 8, 2020research area Knowledge Bases and Search, research area Speech and Natural Language ProcessingWorkshop at EMNLP
The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area. In this paper, we show that question rewriting (QR) of the conversational context allows to shed more light on this phenomenon and also use it to evaluate robustness of different answer selection approaches. We introduce a simple framework that enables an automated analysis of the conversational question...