Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
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Yixin Nie, Songhe Wang, Mohit Bansal. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Prior graph-augmented retrieval work trains a re-ranking module that can traverse a graph made using Wikipedia hyperlinks.
“In this second category, previous work trains a re-ranking module which can traverse a graph made using Wikipedia hyperlinks [16, 100, 54, 14, 4, 44].”
From HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models (2024) · cites this paper as [54]
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Multi-level retrieval with semantic Axiomatic Fuzzy Set clustering for question answering
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Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
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Triple-Fact Retriever: An explainable reasoning retrieval model for multi-hop QA problem
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A syntactic evidence network model for fact verification
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Coarse and Fine Granularity Graph Reasoning for Interpretable Multi-Hop Question Answering
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CosG: A Graph-Based Contrastive Learning Method for Fact Verification
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Multi-Step Reasoning Over Unstructured Text with Beam Dense RetrievalPreprint
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Ni, A., Gardner, M., & Dasigi, P. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.497 · Cited by 10 · see citing works
Multi-Hop Fact Checking of Political Claims
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Weakly Supervised Pre-Training for Multi-Hop Retriever
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Counterfactual Multihop QA: A Cause-Effect Approach for Reducing Disconnected Reasoning
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Learning to Recover Reasoning Chains for Multi-Hop Question Answering via Cooperative Games
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Simple and Efficient ways to Improve REALM
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Generating Followup Questions for Interpretable Multi-hop Question AnsweringPreprint
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Hierarchical Representations in Dense Passage Retrieval for Question-Answering
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LogiLaw Dataset Towards Reinforcement Learning from Logical Feedback (RLLF)
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Complementary Evidence Identification in Open-Domain Question Answering
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Knowledge Transfer between Structured and Unstructured Sources for Complex Question Answering
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- Authors
- Nie, Y., Wang, S., & Bansal, M.
- Affiliations
- University of North Carolina at Chapel Hill (US)
- Venue
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Published
- 2019
- Pages
- 2553-2566
- OpenAlex
- W2970139579
- Cited by
- 132
- References
- 25
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 8 · 2023: 18 · 2022: 25 · 2021: 44 · 2020: 27 · 2019: 2
- FWCI
- 9.37
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- semantic search, machine reading, reading comprehension, large-scale retrieval, question answering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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