What Do You Mean Exactly?: Analyzing Clarification Questions in CQA

By Pavel Braslavski, Denis Savenkov, Eugene Agichtein, Alina Dubatovka. 2017

81 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

AmbigQA: Answering Ambiguous Open-domain Questions (2020, background): “Research on community Q&A (Braslavski et al., 2017; Rao and Daumé III, 2018, 2019) studies finding underspecification in the question, but it does not find the answer to the original question. In recent work, Xu et al. (2019) study clarification of questions that are intentionally annotated with pre”
Asking Clarification Questions in Knowledge-Based Question Answering (2019, background): “Different from these studies, Braslavski et al. (2017) investigate a community question answering (CQA) dataset and study how to predict the speciﬁc subject of a clariﬁcation question.”
MIMICS: A Large-Scale Data Collection for Search Clarification (2020, background): “Clarification has been explored in a number of applications, such as speech recognition [26], dialogue systems [4, 12, 20], and community question answering [5, 23, 24].”
Asking Clarifying Questions in Open-Domain Information-Seeking Conversations (2019, unknown): “People have studied humangenerated dialogues on question answering (QA) websites, analyzing the intent of each utterance [32] and, more specifically, clarifying questions [9].”