Who cites «MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text» · page 3
- Learning To Use Formulas To Solve Simple Arithmetic Problems — our card · 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- MAWPS: A Math Word Problem Repository — 2016 · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Machine Comprehension Using Match-LSTM and Answer Pointer — 2016 · arXiv:1608.07905
- Machine Comprehension using Rich Semantic Representations — 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- SQuAD: 100,000+ Questions for Machine Comprehension of Text — 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- Science Question Answering using Instructional Materials — 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Text Understanding with the Attention Sum Reader Network — 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- The LAMBADA dataset: Word prediction requiring a broad discourse context — 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Understanding Satirical Articles Using Common-Sense — 2016 · Transactions of the Association for Computational Linguistics
- Who did What: A Large-Scale Person-Centered Cloze Dataset — 2016 · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
- A Strong Lexical Matching Method for the Machine Comprehension Test — 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing
- Learning Answer-Entailing Structures for Machine Comprehension — 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)
- Machine Comprehension with Discourse Relations — 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)
- Machine Comprehension with Syntax, Frames, and Semantics — 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)
- Teaching Machines to Read and Comprehend — 2015 · arXiv:1506.03340
- The Goldilocks Principle: Reading Children''s Books with Explicit Memory Representations — 2015 · arXiv:1511.02301
- Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks — our card · 2015 · arXiv:1502.05698
- Fact Checking: Task definition and dataset construction — 2014 · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science
- Memory Networks — 2014 · arXiv:1410.3916
- Modeling Biological Processes for Reading Comprehension — 2014 · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Animacy Detection with Voting Models — 2013 · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing
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- lei-2024-qsnail-questionnaire-dataset-sequential-question
- liu-2021-unsupervised-multiple-choices-question-answering
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- stanovsky-2015-open-ie-as-intermediate-structure
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