Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
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Summary
A new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI.
- Type
- preprint
- Published
- 2018-03-14
- Cited by
- 5,353
- References
- 36
- Access
- Open access
- OpenAlex
- https://openalex.org/W2794325560
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:3922816
Keywords
Arc (geometry), Computer science, Engineering
References
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- Diagram Understanding in Geometry Questions
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- Overview of Todai Robot Project and Evaluation Framework of its NLP-based Problem Solving
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- A Decomposable Attention Model for Natural Language Inference
- Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions
- NewsQA: A Machine Comprehension Dataset
- Tracking the World State with Recurrent Entity Networks
- Answering Complex Questions Using Open Information Extraction
- TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
- How to Write Science Questions that Are Easy for People and Hard for Computers
- Crowdsourcing Multiple Choice Science Questions
- Are You Smarter Than a Sixth Grader? Textbook Question Answering for Multimodal Machine Comprehension
- Constructing Datasets for Multi-hop Reading Comprehension Across Documents
- SciTaiL: A Textual Entailment Dataset from Science Question Answering
- Annotation Artifacts in Natural Language Inference Data
Cited by
- Large-scale Cloze Test Dataset Designed by Teachers
- Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering
- KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
- Estimating Linguistic Complexity for Science Texts
- A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
- Large-scale Cloze Test Dataset Created by Teachers
- Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension
- What Makes Reading Comprehension Questions Easier?
- An Interface for Annotating Science Questions
- Improving Question Answering by Commonsense-Based Pre-Training
- Answering Science Exam Questions Using Query Rewriting with Background Knowledge
- Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
- A Qualitative Comparison of CoQA, SQuAD 2.0 and QuAC
- Improving Machine Reading Comprehension with General Reading Strategies
- Knowledge Representation and Reasoning in Answering Science Questions: A Case Study for Food Web Questions
- QuaRel: A Dataset and Models for Answering Questions about Qualitative Relationships
- Improving Retrieval-Based Question Answering with Deep Inference Models
- Declarative Question Answering over Knowledge Bases containing Natural Language Text with Answer Set Programming
- Advances in Automatically Solving the ENEM
- R-Trans: RNN Transformer Network for Chinese Machine Reading Comprehension
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