---
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/page/3/"
---
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# Who cites «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text» · page 3

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

1. [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) *— 2018 · arXiv:1803.05457*
2. [Towards Inference-Oriented Reading Comprehension: ParallelQA](https://aclanthology.org/W18-1001/) *— 2018 · Proceedings of the Workshop on Generalization in the Age of Deep Learning*
3. [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://arxiv.org/abs/1809.02922) *— 2018 · arXiv:1809.02922*
4. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
5. [Whodunnit? Crime Drama as a Case for Natural Language Understanding](https://aclanthology.org/Q18-1001/) *— 2018 · Transactions of the Association for Computational Linguistics*
6. [Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension](https://aclanthology.org/S18-1120/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation*
7. [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://aclanthology.org/D18-1258/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing*
8. [A Constituent-Centric Neural Architecture for Reading Comprehension](https://aclanthology.org/P17-1129/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks](https://aclanthology.org/I17-1096/) *— 2017 · Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
10. [Apples to Apples: Learning Semantics of Common Entities Through a Novel Comprehension Task](https://aclanthology.org/P17-1084/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [Broad Context Language Modeling as Reading Comprehension](https://aclanthology.org/E17-2009/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers*
12. [Coarse-to-Fine Question Answering for Long Documents](https://aclanthology.org/P17-1020/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Dialog state tracking, a machine reading approach using Memory Network](https://aclanthology.org/E17-1029/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
14. [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://arxiv.org/abs/1711.05073) *— 2017 · arXiv:1711.05073*
15. [Emergent Predication Structure in Hidden State Vectors of Neural Readers](https://aclanthology.org/W17-2604/) *— 2017 · Proceedings of the 2nd Workshop on Representation Learning for NLP*
16. [Evaluation Metrics for Machine Reading Comprehension: Prerequisite Skills and Readability](https://aclanthology.org/P17-1075/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
17. [From Textbooks to Knowledge: A Case Study in Harvesting Axiomatic Knowledge from Textbooks to Solve Geometry Problems](https://aclanthology.org/D17-1081/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
18. [Gated End-to-End Memory Networks](https://aclanthology.org/E17-1001/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
19. [Gated Self-Matching Networks for Reading Comprehension and Question Answering](https://aclanthology.org/P17-1018/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [Learning and Knowledge Transfer with Memory Networks for Machine Comprehension](https://aclanthology.org/E17-1080/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
21. [Learning to Ask: Neural Question Generation for Reading Comprehension](https://arxiv.org/abs/1705.00106) *— 2017 · arXiv:1705.00106*
22. [Learning to Solve Geometry Problems from Natural Language Demonstrations in Textbooks](https://aclanthology.org/S17-1029/) *— 2017 · Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)*
23. [Making Neural QA as Simple as Possible but not Simpler](https://arxiv.org/abs/1703.04816) *— 2017 · arXiv:1703.04816*
24. [Modelling Protagonist Goals and Desires in First-Person Narrative](https://aclanthology.org/W17-5543/) *— 2017 · Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue*
25. [RACE: Large-scale ReAding Comprehension Dataset From Examinations](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/) *— our card · 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
26. [Reasoning with Heterogeneous Knowledge for Commonsense Machine Comprehension](https://aclanthology.org/D17-1216/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
27. [Semi-Supervised QA with Generative Domain-Adaptive Nets](https://aclanthology.org/P17-1096/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://aclanthology.org/P17-1147/) *— 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Which is the Effective Way for Gaokao: Information Retrieval or Neural Networks?](https://aclanthology.org/E17-1011/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
30. [A Corpus and Cloze Evaluation for Deeper Understanding of Commonsense Stories](https://aclanthology.org/N16-1098/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
31. [A Diagram Is Worth A Dozen Images](https://arxiv.org/abs/1603.07396) *— 2016 · arXiv:1603.07396*
32. [A Parallel-Hierarchical Model for Machine Comprehension on Sparse Data](https://aclanthology.org/P16-1041/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task](https://arxiv.org/abs/1606.02858) *— 2016 · arXiv:1606.02858*
34. [An Analysis of Prerequisite Skills for Reading Comprehension](https://aclanthology.org/W16-6001/) *— 2016 · Proceedings of the Workshop on Uphill Battles in Language Processing: Scaling Early Achievements to Robust Methods*
35. [Attention-Based Convolutional Neural Network for Machine Comprehension](https://aclanthology.org/W16-0103/) *— 2016 · Proceedings of the Workshop on Human-Computer Question Answering*
36. [Bidirectional Attention Flow for Machine Comprehension](https://arxiv.org/abs/1611.01603) *— 2016 · arXiv:1611.01603*
37. [Character Identification on Multiparty Conversation: Identifying Mentions of Characters in TV Shows](https://aclanthology.org/W16-3612/) *— 2016 · Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue*
38. [Dynamic Coattention Networks For Question Answering](https://arxiv.org/abs/1611.01604) *— 2016 · arXiv:1611.01604*
39. [Dynamic Entity Representation with Max-pooling Improves Machine Reading](https://aclanthology.org/N16-1099/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
40. [Easy Questions First? A Case Study on Curriculum Learning for Question Answering](https://aclanthology.org/P16-1043/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Joint Learning of Sentence Embeddings for Relevance and Entailment](https://aclanthology.org/W16-1602/) *— 2016 · Proceedings of the 1st Workshop on Representation Learning for NLP*
42. [Learning To Use Formulas To Solve Simple Arithmetic Problems](https://md.research.elamur.ai/free/en/papers/mitra-2016-learning-use-formulas-solve-simple/) *— our card · 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [MAWPS: A Math Word Problem Repository](https://aclanthology.org/N16-1136/) *— 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
44. [Machine Comprehension Using Match-LSTM and Answer Pointer](https://arxiv.org/abs/1608.07905) *— 2016 · arXiv:1608.07905*
45. [Machine Comprehension using Rich Semantic Representations](https://aclanthology.org/P16-2079/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
46. [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://aclanthology.org/D16-1264/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
47. [Science Question Answering using Instructional Materials](https://aclanthology.org/P16-2076/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
48. [Text Understanding with the Attention Sum Reader Network](https://aclanthology.org/P16-1086/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
49. [The LAMBADA dataset: Word prediction requiring a broad discourse context](https://aclanthology.org/P16-1144/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [Understanding Satirical Articles Using Common-Sense](https://aclanthology.org/Q16-1038/) *— 2016 · Transactions of the Association for Computational Linguistics*
51. [Who did What: A Large-Scale Person-Centered Cloze Dataset](https://aclanthology.org/D16-1241/) *— 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing*
52. [A Strong Lexical Matching Method for the Machine Comprehension Test](https://aclanthology.org/D15-1197/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
53. [Learning Answer-Entailing Structures for Machine Comprehension](https://aclanthology.org/P15-1024/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
54. [Machine Comprehension with Discourse Relations](https://aclanthology.org/P15-1121/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
55. [Machine Comprehension with Syntax, Frames, and Semantics](https://aclanthology.org/P15-2115/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
56. [Open IE as an Intermediate Structure for Semantic Tasks](https://aclanthology.org/P15-2050/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
57. [Teaching Machines to Read and Comprehend](https://arxiv.org/abs/1506.03340) *— 2015 · arXiv:1506.03340*
58. [The Goldilocks Principle: Reading Children”s Books with Explicit Memory Representations](https://arxiv.org/abs/1511.02301) *— 2015 · arXiv:1511.02301*
59. [Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks](https://md.research.elamur.ai/free/en/papers/weston-2015-towards-ai-complete-question-answering/) *— our card · 2015 · arXiv:1502.05698*
60. [Fact Checking: Task definition and dataset construction](https://aclanthology.org/W14-2508/) *— 2014 · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science*
61. [Memory Networks](https://arxiv.org/abs/1410.3916) *— 2014 · arXiv:1410.3916*
62. [Modeling Biological Processes for Reading Comprehension](https://aclanthology.org/D14-1159/) *— 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
63. [Animacy Detection with Voting Models](https://aclanthology.org/D13-1006/) *— 2013 · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing*

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