---
title: "Who cites MCTest: A Challenge Dataset for the Open-Domain…"
description: "The papers of the Elamur Research library that cite «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text», found in their…"
canonical: "https://research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/cited-by/"
---
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# Who cites «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text»

263 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/missing-2013-richardson-mctest-challenge-dataset-open-domain/)

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/cited-by/page/3/)

1. [BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks](https://aclanthology.org/2026.acl-long.719/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Multi-Constraint State Tracking with Negation: A Diagnostic Benchmark for LLM World Modeling](https://aclanthology.org/2026.acl-srw.119/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
3. [Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning](https://arxiv.org/abs/2604.12374) *— 2026 · arXiv:2604.12374*
4. [Development of Numerical Error Detection Tasks to Analyze the Numerical Capabilities of Language Models](https://aclanthology.org/2025.coling-main.666/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
5. [FEAT: A Preference Feedback Dataset through a Cost-Effective Auto-Generation and Labeling Framework for English AI Tutoring](https://aclanthology.org/2025.acl-short.45/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
6. [MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning](https://aclanthology.org/2025.coling-main.111/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
7. [NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
8. [SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View](https://aclanthology.org/2025.acl-long.852/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Sub-1B Language Models for Low-Resource Languages: Training Strategies and Insights for Basque](https://aclanthology.org/2025.mrl-main.35/) *— 2025 · Proceedings of the 5th Workshop on Multilingual Representation Learning (MRL 2025)*
10. [WeQA: A Benchmark for Retrieval Augmented Generation in Wind Energy Domain](https://aclanthology.org/2025.nlp4pi-1.20/) *— 2025 · Proceedings of the Fourth Workshop on NLP for Positive Impact (NLP4PI)*
11. [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://aclanthology.org/2025.acl-long.169/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://aclanthology.org/2025.acl-long.675/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [\textit D-GEN: Automatic Distractor Generation and Evaluation for Reliable Assessment of Generative Models](https://aclanthology.org/2025.findings-acl.174/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
14. [An Information-Theoretic Approach to Analyze NLP Classification Tasks](https://aclanthology.org/2024.acl-long.32/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Are self-explanations from Large Language Models faithful?](https://aclanthology.org/2024.findings-acl.19/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
16. [Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension](https://aclanthology.org/2024.bionlp-1.20/) *— 2024 · Proceedings of the 23rd Workshop on Biomedical Natural Language Processing*
17. [Context-Aware Question Answering in Urdu](https://aclanthology.org/2024.icnlsp-1.26/) *— 2024 · Proceedings of the 7th International Conference on Natural Language and Speech Processing (ICNLSP 2024)*
18. [Distractor Generation in Multiple-Choice Tasks: A Survey of Methods, Datasets, and Evaluation](https://aclanthology.org/2024.emnlp-main.799/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
19. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
20. [Interpreting Themes from Educational Stories](https://aclanthology.org/2024.lrec-main.805/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
21. [LFED: A Literary Fiction Evaluation Dataset for Large Language Models](https://aclanthology.org/2024.lrec-main.915/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
22. [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://aclanthology.org/2024.findings-naacl.130/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
23. [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://aclanthology.org/2024.acl-short.55/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
24. [Qsnail: A Questionnaire Dataset for Sequential Question Generation](https://aclanthology.org/2024.lrec-main.1174/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
25. [Story-Yarn : An Interactive Story Building Application](https://aclanthology.org/2024.icon-1.28/) *— 2024 · Proceedings of the 21st International Conference on Natural Language Processing (ICON)*
26. [TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya](https://aclanthology.org/2024.lrec-main.1404/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
27. [大语言模型故事理解能力评价数据集(Benchmarking story comprehension ability of large language model)](https://aclanthology.org/2024.ccl-1.60/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
28. [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://arxiv.org/abs/2308.00002) *— 2023 · arXiv:2308.00002*
29. [Are NLP Models Good at Tracing Thoughts: An Overview of Narrative Understanding](https://aclanthology.org/2023.findings-emnlp.677/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
30. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
31. [Difficulty-Controllable Neural Question Generation for Reading Comprehension using Item Response Theory](https://aclanthology.org/2023.bea-1.10/) *— 2023 · Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)*
32. [Diversity Enhanced Narrative Question Generation for Storybooks](https://aclanthology.org/2023.emnlp-main.31/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
33. [Domain Incremental Lifelong Learning in an Open World](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
34. [Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://aclanthology.org/2023.findings-emnlp.866/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
35. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [Few-shot Unified Question Answering: Tuning Models or Prompts?](https://aclanthology.org/2023.findings-emnlp.550/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
37. [Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision](https://aclanthology.org/2023.emnlp-main.1001/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
38. [Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks](https://aclanthology.org/2023.jlcl-2.1/) *— 2023 · Journal for Language Technology and Computational Linguistics, Vol. 36 No. 2*
39. [KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding](https://aclanthology.org/2023.findings-acl.743/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
40. [Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions](https://arxiv.org/abs/2308.11483) *— 2023 · arXiv:2308.11483*
41. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension](https://aclanthology.org/2023.findings-emnlp.343/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
43. [Machine Reading Comprehension for Vietnamese Customer Reviews: Task, Corpus and Baseline Models](https://aclanthology.org/2023.paclic-1.3/) *— 2023 · Proceedings of the 37th Pacific Asia Conference on Language, Information and Computation*
44. [Machine Reading Comprehension using Case-based Reasoning](https://aclanthology.org/2023.findings-emnlp.564/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
45. [PARROT: Zero-Shot Narrative Reading Comprehension via Parallel Reading](https://aclanthology.org/2023.findings-emnlp.895/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
46. [Personality Understanding of Fictional Characters during Book Reading](https://aclanthology.org/2023.acl-long.826/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
48. [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) *— 2023 · arXiv:2308.16884 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024*
49. [Using contradictions improves question answering systems](https://aclanthology.org/2023.acl-short.72/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
50. [A Corpus for Commonsense Inference in Story Cloze Test](https://aclanthology.org/2022.lrec-1.375/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
51. [Answer Quality Aware Aggregation for Extractive QA Crowdsourcing](https://aclanthology.org/2022.findings-emnlp.457/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
52. [Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension](https://aclanthology.org/2022.findings-acl.82/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
53. [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://arxiv.org/abs/2208.03299) *— 2022 · arXiv:2208.03299*
54. [BehanceQA: A New Dataset for Identifying Question-Answer Pairs in Video Transcripts](https://aclanthology.org/2022.lrec-1.796/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
55. [DTW at Qur”an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://aclanthology.org/2022.osact-1.10/) *— 2022 · Proceedinsg of the 5th Workshop on Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection*
56. [DocInfer: Document-level Natural Language Inference using Optimal Evidence Selection](https://aclanthology.org/2022.emnlp-main.51/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
57. [Feeding What You Need by Understanding What You Learned](https://aclanthology.org/2022.acl-long.403/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [FrenchMedMCQA: A French Multiple-Choice Question Answering Dataset for Medical domain](https://aclanthology.org/2022.louhi-1.5/) *— 2022 · Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (LOUHI)*
59. [HRCA+: Advanced Multiple-choice Machine Reading Comprehension Method](https://aclanthology.org/2022.lrec-1.651/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
60. [Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge](https://aclanthology.org/2022.acl-long.598/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
61. [Is GPT-3 Text Indistinguishable from Human Text? Scarecrow: A Framework for Scrutinizing Machine Text](https://aclanthology.org/2022.acl-long.501/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [MixQG: Neural Question Generation with Mixed Answer Types](https://aclanthology.org/2022.findings-naacl.111/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
63. [NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization](https://aclanthology.org/2022.findings-emnlp.14/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
64. [Narrative Datasets through the Lenses of NLP and HCI](https://aclanthology.org/2022.hcinlp-1.7/) *— 2022 · Proceedings of the Second Workshop on Bridging Human–Computer Interaction and Natural Language Processing*
65. [Narrative Why-Question Answering: A Review of Challenges and Datasets](https://aclanthology.org/2022.gem-1.48/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
66. [Polyglot Prompt: Multilingual Multitask Prompt Training](https://aclanthology.org/2022.emnlp-main.674/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
67. [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://aclanthology.org/2022.naacl-main.313/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
68. [QuALITY: Question Answering with Long Input Texts, Yes!](https://aclanthology.org/2022.naacl-main.391/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
69. [RealTime QA: What”s the Answer Right Now?](https://arxiv.org/abs/2207.13332) *— 2022 · arXiv:2207.13332*
70. [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://aclanthology.org/2022.naacl-main.317/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
71. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
72. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
73. [Zero-shot cross-lingual open domain question answering](https://aclanthology.org/2022.mia-1.9/) *— 2022 · Proceedings of the Workshop on Multilingual Information Access (MIA)*
74. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
75. [Can NLI Models Verify QA Systems' Predictions?](https://aclanthology.org/2021.findings-emnlp.324/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*
76. [Controlling Text Edition by Changing Answers of Specific Questions](https://aclanthology.org/2021.findings-acl.110/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
77. [DocNLI: A Large-scale Dataset for Document-level Natural Language Inference](https://aclanthology.org/2021.findings-acl.435/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
78. [Does Structure Matter? Encoding Documents for Machine Reading Comprehension](https://aclanthology.org/2021.naacl-main.367/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
79. [English Machine Reading Comprehension Datasets: A Survey](https://md.research.elamur.ai/free/en/papers/dzendzik-2021-english-machine-reading-comprehension-datasets/) *— our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
80. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
81. [GCRC: A New Challenging MRC Dataset from Gaokao Chinese for Explainable Evaluation](https://aclanthology.org/2021.findings-acl.113/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
82. [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://aclanthology.org/2021.tacl-1.57/) *— 2021*
83. [Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources](https://aclanthology.org/2021.findings-acl.197/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
84. [Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction](https://arxiv.org/abs/2104.00563) *— 2021 · arXiv:2104.00563*
85. [Learning from Limited Labels for Long Legal Dialogue](https://aclanthology.org/2021.nllp-1.20/) *— 2021 · Proceedings of the Natural Legal Language Processing Workshop 2021*
86. [MIMOQA: Multimodal Input Multimodal Output Question Answering](https://aclanthology.org/2021.naacl-main.418/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
87. [Memory-efficient Transformers via Top-k Attention](https://aclanthology.org/2021.sustainlp-1.5/) *— 2021 · Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing*
88. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
89. [Model Agnostic Answer Reranking System for Adversarial Question Answering](https://aclanthology.org/2021.eacl-srw.8/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop*
90. [Multi-Strategy Knowledge Distillation Based Teacher-Student Framework for Machine Reading Comprehension](https://aclanthology.org/2021.ccl-1.91/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
91. [On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study](https://aclanthology.org/2021.acl-long.517/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
92. [PROST: Physical Reasoning about Objects through Space and Time](https://aclanthology.org/2021.findings-acl.404/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
93. [ParsiNLU: A Suite of Language Understanding Challenges for Persian](https://aclanthology.org/2021.tacl-1.68/) *— 2021 · Transactions of the Association for Computational Linguistics*
94. [Sorting through the noise: Testing robustness of information processing in pre-trained language models](https://aclanthology.org/2021.emnlp-main.119/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
95. [Topic Knowledge Acquisition and Utilization for Machine Reading Comprehension in Social Media Domain](https://aclanthology.org/2021.ccl-1.88/) *— 2021 · Proceedings of the 20th Chinese National Conference on Computational Linguistics*
96. [Uncertain Local-to-Global Networks for Document-Level Event Factuality Identification](https://aclanthology.org/2021.emnlp-main.207/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
97. [Unsupervised Multiple Choices Question Answering: Start Learning from Basic Knowledge](https://aclanthology.org/2021.mrqa-1.12/) *— 2021 · Proceedings of the 3rd Workshop on Machine Reading for Question Answering*
98. [Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models](https://aclanthology.org/2021.acl-short.43/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
99. [Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation](https://aclanthology.org/2021.sigdial-1.14/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
100. [Winnowing Knowledge for Multi-choice Question Answering](https://aclanthology.org/2021.findings-emnlp.100/) *— 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021*

Pages: **1** · [2](https://md.research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/cited-by/page/2/) · [3](https://md.research.elamur.ai/free/en/papers/missing-2013-richardson-mctest-challenge-dataset-open-domain/cited-by/page/3/)