Open-Retrieval Conversational Question Answering

By Chen Qu, Liu Yang, Cen Chen, Minghui Qiu, W. Bruce Croft, Mohit Iyyer. 2020

207 citations recorded by the source corpus.

Conversational search is one of the ultimate goals of information retrieval. Recent research approaches conversational search by simplified settings of response ranking and conversational question answering, where an answer is either selected from a given candidate set or extracted from a given passage. These simplifications neglect the fundamental role of retrieval in conversational search. To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems. We create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers. Our extensive experiments on OR-QuAC demonstrate that a learnable retriever is crucial for ORConvQA. We further show that our system can make a substantial improvement when we enable history modeling in all system components. Moreover, we show that the reranker component contributes to the model performance by providing a regularization effect. Finally, further in-depth analyses are performed to provide new insights into ORConvQA.

Subfields: Artificial Intelligence, Information Retrieval & Search, Question Answering & Reading Comprehension

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Citations in the corpus

Achieving Human Parity on Visual Question Answering (2021, background): “Recently, open-domain QA [53, 58, 78, 96, 97] has become one of the most popular topics in question answering.”
Dialog Inpainting: Turning Documents into Dialogs (2022, methodology): “Datasets We use four open-domain conversational QA retrieval benchmarks: OR-QuAC (Qu et al., 2020), TREC CAsT-19 (Byrne et al., 2019), TREC CAsT-20 (Dalton et al., 2020), and QReCC (Anantha et al., 2021).”
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning (2021, methodology): “A few recent works (Dalton et al., 2020; Qu et al., 2020) collect retrieval datasets for conversational”
Ditch the Gold Standard: Re-evaluating Conversational Question Answering (2021, methodology): “Chen et al. (2020) propose a recurrent graph neural network on top of BERT embeddings to model the dependencies between the question, the history and the passage.”
Large Dual Encoders Are Generalizable Retrievers (2021, background): “…being quickly adopted by the community and achieving state-of-the-art performance on multiple retrieval tasks, such as passage retrieval (Karpukhin et al., 2020), question answering (Ahmad et al., 2019), conversational question answering (Qu et al., 2020) and bitext retrieval (Feng et al., 2020).”
LocAgent: Graph-Guided LLM Agents for Code Localization (2025, unknown): “It also necessitates reasoning capabilities to analyze the issue, while considering the structural and semantic properties of code (Lewis et al., 2020; Guu et al., 2020; Qu et al., 2020).”