Know What You Don’t Know: Unanswerable Questions for SQuAD
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Summary
SQuadRUn is a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones.
- Type
- preprint
- Published
- 2018-06-11
- Cited by
- 3,424
- References
- 25
- Access
- Open access
- OpenAlex
- https://openalex.org/W2798836595
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:47018994
Keywords
Paragraph, Context (archaeology), Task (project management), Computer science, Question answering
References
- Teaching Machines to Read and Comprehend
- A large annotated corpus for learning natural language inference
- Question Answering Using Enhanced Lexical Semantic Models
- What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
- MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text
- Daemo: A Self-Governed Crowdsourcing Marketplace
- A SICK cure for the evaluation of compositional distributional semantic models
- WikiQA: A Challenge Dataset for Open-Domain Question Answering
- WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia
- NewsQA: A Machine Comprehension Dataset
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
- RACE: Large-scale ReAding Comprehension Dataset From Examinations
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
- Zero-Shot Relation Extraction via Reading Comprehension
- Making Neural QA as Simple as Possible but not Simpler
- Gated Self-Matching Networks for Reading Comprehension and Question Answering
- Position-aware Attention and Supervised Data Improve Slot Filling
- Simple and Effective Multi-Paragraph Reading Comprehension
- Deep Contextualized Word Representations
- MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
Cited by
- Survey on evaluation methods for dialogue systems
- Right Answer for the Wrong Reason: Discovery and Mitigation
- Deep Reinforcement Learning for Sequence-to-Sequence Models
- Pathologies of Neural Models Make Interpretations Difficult
- CoQA: A Conversational Question Answering Challenge
- QuAC: Question Answering in Context
- Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
- What Makes Reading Comprehension Questions Easier?
- HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
- A dataset and baselines for sequential open-domain question answering
- Knowledge Based Machine Reading Comprehension
- Answering Science Exam Questions Using Query Rewriting with Background Knowledge
- Transforming Question Answering Datasets Into Natural Language Inference Datasets
- A Nil-Aware Answer Extraction Framework for Question Answering
- I Know What You Want: Semantic Learning for Text Comprehension
- ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
- A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
- Stochastic Answer Networks for SQuAD 2.0
- Summarization Filter: Consider More About the Whole Query in Machine Comprehension
- pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
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