CoQA: A Conversational Question Answering Challenge
Speaker
Siva Reddy
Stanford University
Host
Regina Barzilay
MIT CSAIL
Title: CoQA: A Conversational Question Answering Challenge
Abstract:
Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. In this talk, I will present our work on CoQA, a novel dataset for building Conversational Question Answering systems. CoQA contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa/
Bio:
Siva Reddy is a postdoc in Computer Science at Stanford University working with Prof. Christopher Manning. His research focuses on enabling natural communication between humans and machines. Prior to the postdoc, he was a Google PhD Fellow at the University of Edinburgh under the supervision of Prof. Mirella Lapata and Prof. Mark Steedman.
Abstract:
Humans gather information by engaging in conversations involving a series of interconnected questions and answers. For machines to assist in information gathering, it is therefore essential to enable them to answer conversational questions. In this talk, I will present our work on CoQA, a novel dataset for building Conversational Question Answering systems. CoQA contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage. We analyze CoQA in depth and show that conversational questions have challenging phenomena not present in existing reading comprehension datasets, e.g., coreference and pragmatic reasoning. We evaluate strong conversational and reading comprehension models on CoQA. The best system obtains an F1 score of 65.1%, which is 23.7 points behind human performance (88.8%), indicating there is ample room for improvement. We launch CoQA as a challenge to the community at https://stanfordnlp.github.io/coqa/
Bio:
Siva Reddy is a postdoc in Computer Science at Stanford University working with Prof. Christopher Manning. His research focuses on enabling natural communication between humans and machines. Prior to the postdoc, he was a Google PhD Fellow at the University of Edinburgh under the supervision of Prof. Mirella Lapata and Prof. Mark Steedman.