--- title: "Who cites RACE: Large-scale ReAding Comprehension Dataset…" description: "The papers of the Elamur Research library that cite «RACE: Large-scale ReAding Comprehension Dataset From Examinations», found in their bibliographies." canonical: "https://research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [RACE: Large-scale ReAding Comprehension Dataset…](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/) / cited by # Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 7 619 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/lai-2017-race-large-scale-reading-comprehension/) Pages: [1](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/) · … · [6](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/) · **7** 1. [Know What You Don”t Know: Unanswerable Questions for SQuAD](https://aclanthology.org/P18-2124/) *— 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)* 2. [Large-scale Cloze Test Dataset Created by Teachers](https://aclanthology.org/D18-1257/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing* 3. [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://md.research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/) *— our card · 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)* 4. [MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge](https://aclanthology.org/L18-1564/) *— 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)* 5. [Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension](https://aclanthology.org/D18-1238/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing* 6. [Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension](https://aclanthology.org/C18-1073/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics* 7. [Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds](https://md.research.elamur.ai/free/en/papers/labutov-2018-multi-relational-question-answering-narratives/) *— our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)* 8. [Neural Approaches to Conversational AI](https://arxiv.org/abs/1809.08267) *— 2018 · arXiv:1809.08267* 9. [ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension](https://arxiv.org/abs/1810.12885) *— 2018 · arXiv:1810.12885* 10. [Reading Comprehension with Graph-based Temporal-Casual Reasoning](https://aclanthology.org/C18-1069/) *— 2018 · Proceedings of the 27th International Conference on Computational Linguistics* 11. [SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge](https://aclanthology.org/S18-1119/) *— 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation* 12. [The Web as a Knowledge-Base for Answering Complex Questions](https://aclanthology.org/N18-1059/) *— 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)* 13. [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://arxiv.org/abs/1809.02922) *— 2018 · arXiv:1809.02922* 14. [What Makes Reading Comprehension Questions Easier?](https://aclanthology.org/D18-1453/) *— 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing* 15. [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* 16. [DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications](https://arxiv.org/abs/1711.05073) *— 2017 · arXiv:1711.05073* 17. [Dynamic Fusion Networks for Machine Reading Comprehension](https://arxiv.org/abs/1711.04964) *— 2017 · arXiv:1711.04964* 18. [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)* 19. [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://arxiv.org/abs/1611.09268) *— 2016 · arXiv:1611.09268* Pages: [1](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/) · … · [6](https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/6/) · **7**