Open-Domain Question-Answering

By John M. Prager. 2007

242 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Retrieval & Search, Natural Language Processing, Philosophy

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Citations in the corpus

How Much Knowledge Can You Pack into the Parameters of a Language Model? (2020, background): “A more difficult variant is “open-domain question answering” (Prager, 2006), where the model can be asked arbitrary context-independent questions (e.”
Modelling input texts: from Tree Kernels to Deep Learning (2015, background): “The question focus is typically a simple noun representing the entity or property being sought by the question [117].”
A Long Short-Term Memory Model for Answer Sentence Selection in Question Answering (2015, background): “…of open-domain question answering (QA) systems is composed of three high level major steps: a) question analysis and retrieval of candidate passages; b) ranking and selecting of passages which contain the answer; and optionally c) extracting and verifying the answer (Prager, 2006; Ferrucci, 2012).”
Retrieval-based Language Models and Applications (2023, background): “• ACL 2020 tutorial on Open-domain QA (Chen and Yih, 2020): This tutorial provides comprehensive reviews of open-domain question answering, some of which consist of a re-triever and a generative model, while we focus on the recent progress of architectures and learning algorithms of retrieval-based…”
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions (2020, background): “Most of the recent works view multi-hop evidence collection as an iterative document retrieval problem (Asai et al. 2020; Feldman and El-Yaniv 2019; Das et al. 2019a), which can be decomposed to several single-step document retrieval.”
RealTime QA: What's the Answer Right Now? (2022, background): “This dynamic scheme contrasts with the well-established QA annotations (Chen et al., 2017; Chen and Yih, 2020) that are per-a r X i v : 2207 .”
Retrieval-Augmented Generation for Large Language Models: A Survey (2023, background): “It seeks to provide a comprehensive overview and analysis of existing RAG technologies and offer conclusions and prospects for future development methods.”