Who cites «RACE: Large-scale ReAding Comprehension Dataset From Examinations» · page 7
- Know What You Don”t Know: Unanswerable Questions for SQuAD — 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Large-scale Cloze Test Dataset Created by Teachers — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences — 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)
- MCScript: A Novel Dataset for Assessing Machine Comprehension Using Script Knowledge — 2018 · Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
- Multi-Granular Sequence Encoding via Dilated Compositional Units for Reading Comprehension — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
- Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds — our card · 2018 · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Neural Approaches to Conversational AI — 2018 · arXiv:1809.08267
- ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension — 2018 · arXiv:1810.12885
- Reading Comprehension with Graph-based Temporal-Casual Reasoning — 2018 · Proceedings of the 27th International Conference on Computational Linguistics
- SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
- The Web as a Knowledge-Base for Answering Complex Questions — 2018 · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Transforming Question Answering Datasets Into Natural Language Inference Datasets — 2018 · arXiv:1809.02922
- What Makes Reading Comprehension Questions Easier? — 2018 · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Yuanfudao at SemEval-2018 Task 11: Three-way Attention and Relational Knowledge for Commonsense Machine Comprehension — 2018 · Proceedings of the 12th International Workshop on Semantic Evaluation
- DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications — 2017 · arXiv:1711.05073
- Dynamic Fusion Networks for Machine Reading Comprehension — 2017 · arXiv:1711.04964
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension — 2017 · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset — 2016 · arXiv:1611.09268
This page in Markdown → https://md.research.elamur.ai/free/en/papers/lai-2017-race-large-scale-reading-comprehension/cited-by/page/7/