---
title: "Who cites Large-Scale QA-SRL Parsing"
description: "The papers of the Elamur Research library that cite «Large-Scale QA-SRL Parsing», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/fitzgerald-2018-large-scale-qa-srl-parsing/cited-by/"
---
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# Who cites «Large-Scale QA-SRL Parsing»

58 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/fitzgerald-2018-large-scale-qa-srl-parsing/)

1. [Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages](https://aclanthology.org/2026.eacl-long.112/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Modelling Analogies and Analogical Reasoning: Connecting Cognitive Science Theory and NLP Research](https://aclanthology.org/2026.tacl-1.32/) *— 2026 · Transactions of the Association for Computational Linguistics*
3. [SocraticKG: Knowledge Graph Construction via QA-Driven Fact Extraction](https://aclanthology.org/2026.findings-acl.1951/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
4. [ASQ: Automatically Generating Question-Answer Pairs Using AMRs](https://aclanthology.org/2025.ranlp-1.115/) *— 2025 · Proceedings of the 15th International Conference on Recent Advances in Natural Language Processing - Natural Language Processing in the Generative AI Era*
5. [Are LLMs Good for Semantic Role Labeling via Question Answering?: A Preliminary Analysis](https://aclanthology.org/2025.ijcnlp-srw.21/) *— 2025 · The 14th International Joint Conference on Natural Language Processing and The 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
6. [QA-Noun: Representing Nominal Semantics via Natural Language Question-Answer Pairs](https://aclanthology.org/2025.ijcnlp-long.147/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
7. [Semantic Bridge: Universal Multi-Hop Question Generation via AMR-Driven Graph Synthesis](https://arxiv.org/abs/2508.10013) *— 2025 · arXiv:2508.10013*
8. [A Survey on Open Information Extraction from Rule-based Model to Large Language Model](https://aclanthology.org/2024.findings-emnlp.560/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
9. [Explicating the Implicit: Argument Detection Beyond Sentence Boundaries](https://aclanthology.org/2024.acl-long.863/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [Promptly Predicting Structures: The Return of Inference](https://aclanthology.org/2024.naacl-long.7/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
11. [QAEVENT: Event Extraction as Question-Answer Pairs Generation](https://aclanthology.org/2024.findings-eacl.126/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
12. [Bootstrapping Small & High Performance Language Models with Unmasking-Removal Training Policy](https://aclanthology.org/2023.emnlp-main.30/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
13. [Dense Retrieval as Indirect Supervision for Large-space Decision Making](https://aclanthology.org/2023.findings-emnlp.1002/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
14. [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://aclanthology.org/2023.tacl-1.57/) *— 2023*
15. [Learning Semantic Role Labeling from Compatible Label Sequences](https://aclanthology.org/2023.findings-emnlp.1041/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
16. [Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering](https://aclanthology.org/2023.acl-long.110/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [Preserving Knowledge Invariance: Rethinking Robustness Evaluation of Open Information Extraction](https://aclanthology.org/2023.emnlp-main.360/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
18. [PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition](https://aclanthology.org/2023.findings-acl.565/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
19. [QA-Adj: Adding Adjectives to QA-based Semantics](https://aclanthology.org/2023.dmr-1.8/) *— 2023 · Proceedings of the Fourth International Workshop on Designing Meaning Representations*
20. [The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP](https://aclanthology.org/2023.bigpicture-1.4/) *— 2023 · Proceedings of the Big Picture Workshop*
21. [A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations](https://aclanthology.org/2022.findings-naacl.37/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
22. [Draw Me a Flower: Processing and Grounding Abstraction in Natural Language](https://aclanthology.org/2022.tacl-1.77/) *— 2022 · Transactions of the Association for Computational Linguistics*
23. [Life is a Circus and We are the Clowns: Automatically Finding Analogies between Situations and Processes](https://aclanthology.org/2022.emnlp-main.232/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
24. [QASem Parsing: Text-to-text Modeling of QA-based Semantics](https://aclanthology.org/2022.emnlp-main.528/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
25. [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://aclanthology.org/2022.findings-emnlp.313/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
26. [Syntactically Rich Discriminative Training: An Effective Method for Open Information Extraction](https://aclanthology.org/2022.emnlp-main.401/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
27. [Transfer Learning from Semantic Role Labeling to Event Argument Extraction with Template-based Slot Querying](https://aclanthology.org/2022.emnlp-main.169/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
28. [“What makes a question inquisitive?” A Study on Type-Controlled Inquisitive Question Generation](https://aclanthology.org/2022.starsem-1.22/) *— 2022 · Proceedings of the 11th Joint Conference on Lexical and Computational Semantics*
29. [Asking It All: Generating Contextualized Questions for any Semantic Role](https://aclanthology.org/2021.emnlp-main.108/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
30. [Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence](https://aclanthology.org/2021.findings-acl.371/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
31. [Foreseeing the Benefits of Incidental Supervision](https://aclanthology.org/2021.emnlp-main.134/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
32. [Inducing Semantic Roles Without Syntax](https://aclanthology.org/2021.findings-acl.389/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
33. [LSOIE: A Large-Scale Dataset for Supervised Open Information Extraction](https://aclanthology.org/2021.eacl-main.222/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
34. [PAQ: 65 Million Probably-Asked Questions and What You Can Do With Them](https://aclanthology.org/2021.tacl-1.65/) *— 2021 · Transactions of the Association for Computational Linguistics*
35. [QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions](https://aclanthology.org/2021.emnlp-main.778/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
36. [Sister Help: Data Augmentation for Frame-Semantic Role Labeling](https://aclanthology.org/2021.law-1.8/) *— 2021 · Proceedings of the Joint 15th Linguistic Annotation Workshop (LAW) and 3rd Designing Meaning Representations (DMR) Workshop*
37. [WeaQA: Weak Supervision via Captions for Visual Question Answering](https://aclanthology.org/2021.findings-acl.302/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
38. [What Did You Refer to? Evaluating Co-References in Dialogue](https://aclanthology.org/2021.findings-acl.450/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
39. [A Novel Workflow for Accurately and Efficiently Crowdsourcing Predicate Senses and Argument Labels](https://aclanthology.org/2020.findings-emnlp.38/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
40. [Asking without Telling: Exploring Latent Ontologies in Contextual Representations](https://aclanthology.org/2020.emnlp-main.552/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
41. [Break It Down: A Question Understanding Benchmark](https://aclanthology.org/2020.tacl-1.13/) *— 2020 · Transactions of the Association for Computational Linguistics*
42. [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://md.research.elamur.ai/free/en/papers/roit-2020-controlled-crowdsourcing-high-quality-qa/) *— our card · 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
43. [Evaluating Factuality in Generation with Dependency-level Entailment](https://aclanthology.org/2020.findings-emnlp.322/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
44. [Event Extraction as Machine Reading Comprehension](https://aclanthology.org/2020.emnlp-main.128/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
45. [Inquisitive Question Generation for High Level Text Comprehension](https://md.research.elamur.ai/free/en/papers/ko-2020-inquisitive-question-generation-high-level/) *— our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
46. [Leveraging Non-Specialists for Accurate and Time Efficient AMR Annotation](https://aclanthology.org/2020.cllrd-1.5/) *— 2020 · Proceedings of the LREC 2020 Workshop on “Citizen Linguistics in Language Resource Development*
47. [Linguistic Appropriateness and Pedagogic Usefulness of Reading Comprehension Questions](https://aclanthology.org/2020.lrec-1.217/) *— 2020 · Proceedings of the Twelfth Language Resources and Evaluation Conference*
48. [Machine Knowledge: Creation and Curation of Comprehensive Knowledge Bases](https://arxiv.org/abs/2009.11564) *— 2020 · arXiv:2009.11564 (Foundations and Trends in Databases, 2021)*
49. [QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://aclanthology.org/2020.emnlp-main.224/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
50. [QANom: Question-Answer driven SRL for Nominalizations](https://md.research.elamur.ai/free/en/papers/klein-2020-qanom-question-answer-driven-srl/) *— our card · 2020 · Proceedings of the 28th International Conference on Computational Linguistics*
51. [QuASE: Question-Answer Driven Sentence Encoding](https://aclanthology.org/2020.acl-main.772/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
52. [The Extraordinary Failure of Complement Coercion Crowdsourcing](https://aclanthology.org/2020.insights-1.17/) *— 2020 · Proceedings of the First Workshop on Insights from Negative Results in NLP*
53. [Divide and Extract – Disentangling Clause Splitting and Proposition Extraction](https://aclanthology.org/R19-1047/) *— 2019 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)*
54. [Learning and Evaluating General Linguistic Intelligence](https://arxiv.org/abs/1901.11373) *— 2019 · arXiv:1901.11373*
55. [Machine Comprehension Improves Domain-Specific Japanese Predicate-Argument Structure Analysis](https://aclanthology.org/D19-5814/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
56. [Semantic Role Labeling with Pretrained Language Models for Known and Unknown Predicates](https://aclanthology.org/R19-1073/) *— 2019 · Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)*
57. [Understanding Roles and Entities: Datasets and Models for Natural Language Inference](https://arxiv.org/abs/1904.09720) *— 2019 · arXiv:1904.09720*
58. [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://aclanthology.org/P18-2058/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*