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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.
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.
Towards Understanding and Harnessing the Potential of Clause Learning
The first precise characterization of clause learning as a proof system (CL) is presented, and it is shown that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution satisfying a natural property.
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.
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization
A randomized algorithm is proposed that gives a constant-factor approximation of a general discrete integral defined over an exponentially large set and demonstrates that with a small number of MAP queries the authors can efficiently approximate the partition function of discrete graphical models.
Parsing Algebraic Word Problems into Equations
- Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, S. Ang
- Computer Science, MathematicsTACL
- 18 December 2015
This paper formalizes the problem of solving multi-sentence algebraic word problems as that of generating and scoring equation trees. We use integer linear programming to generate equation trees and…
Algorithm Selection and Scheduling
- Serdar Kadioglu, Y. Malitsky, Ashish Sabharwal, Horst Samulowitz, Meinolf Sellmann
- Computer ScienceCP
- 12 September 2011
This work proposes various static as well as dynamic scheduling strategies, and demonstrates that in comparison to pure algorithm selection, this novel combination of scheduling and solver selection can significantly boost performance.
Model Counting: A New Strategy for Obtaining Good Bounds
This work presents a new approach to model counting that is based on adding a carefully chosen number of so-called streamlining constraints to the input formula in order to cut down the size of its solution space in a controlled manner.