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Rule Induction with CN2: Some Recent Improvements
Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated.
The CN2 Induction Algorithm
A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present.
The Sixth PASCAL Recognizing Textual Entailment Challenge
This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which TextualEntailment is performed on a real corpus in the Update Summarization scenario.
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
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.
The CN2 induction algorithm
A description and empirical evaluation of a new induction system, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present, is presented.
SciTaiL: A Textual Entailment Dataset from Science Question Answering
A new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem, is presented, and it is demonstrated that one can improve accuracy on SCITAIL by 5% using a new neural model that exploits linguistic structure.
UnifiedQA: Crossing Format Boundaries With a Single QA System
This work uses the latest advances in language modeling to build a single pre-trained QA model, UNIFIEDQA, that performs well across 19 QA datasets spanning 4 diverse formats, and results in a new state of the art on 10 factoid and commonsense question answering datasets.
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
A new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject, and oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts.
QASC: A Dataset for Question Answering via Sentence Composition
- Tushar Khot, Peter Clark, Michal Guerquin, Peter Alexander Jansen, Ashish Sabharwal
- Computer ScienceAAAI
- 25 October 2019
This work presents a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question, and provides annotation for supporting facts as well as their composition.
Transformers as Soft Reasoners over Language
This work trains transformers to reason (or emulate reasoning) over natural language sentences using synthetically generated data, thus bypassing a formal representation and suggesting a new role for transformers, namely as limited "soft theorem provers" operating over explicit theories in language.