A dataset and baselines for sequential open-domain question answering

By Ahmed Elgohary, Chen Zhao, Jordan L. Boyd-Graber. 2018

47 citations recorded by the source corpus.

Previous work on question-answering systems mainly focuses on answering individual questions, assuming they are independent and devoid of context. Instead, we investigate sequential question answering, asking multiple related questions. We present QBLink, a new dataset of fully human-authored questions. We extend existing strong question answering frameworks to include previous questions to improve the overall question-answering accuracy in open-domain question answering. The dataset is publicly available at http://sequential.qanta.org.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

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

In Artificial Intelligence

In Natural Language Processing

Citations in the corpus

Technical report on Conversational Question Answering (2019, methodology): “Recently, several CQA datasets were proposed, such as CoQA(Reddy et al., 2018), QuAC (Choi et al., 2018) and QBLink (Elgohary et al., 2018).”
CoQA: A Conversational Question Answering Challenge (2018, background): “Elgohary et al. (2018) proposed a sequential question answering dataset collected from Quiz Bowl tournaments, where a sequence contains multiple related questions.”
SDNet: Contextualized Attention-based Deep Network for Conversational Question Answering (2018, unknown): “To incorporate conversation into reading comprehension, recently there are several public datasets that evaluate QA model’s efﬁcacy in conversational setting, such as CoQA (Reddy et al., 2018), QuAC (Choi et al., 2018) and QBLink (Elgohary et al., 2018).”
What Question Answering can Learn from Trivia Nerds (2019, unknown): “, 2018); and other work explicitly focusing on identifying useful clarification questions (Rao and Daumé III), thematically linked questions (Elgohary et al., 2018) or resolving ambiguities that arise from coreference or pragmatic constraints by rewriting underspecified question strings (Elgohary et”
CONVERSATIONAL MACHINE COMPREHENSION (2019, background): “Recent datasets (Choi et al., 2018; Reddy et al., 2019; Elgohary et al., 2018; Saeidi et al., 2018) enabled studying it in the textual setting, where the information source used to answer questions is a given article.”
FlowQA: Grasping Flow in History for Conversational Machine Comprehension (2018, background): “Recent datasets (Choi et al., 2018; Reddy et al., 2018; Elgohary et al., 2018; Saeidi et al., 2018) enabled studying it in the textual setting, where the information source used to answer questions is a given article.”
Can You Unpack That? Learning to Rewrite Questions-in-Context (2019, methodology): “Recent work in CQA has used simple concatenation (Elgohary et al., 2018), sequential neural models (Huang et al., 2019), and transformers (Qu et al., 2019a) for modeling the interaction between the conversation history, the question and reference documents.”