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Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems
Shyam Upadhyay
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Ming-Wei Chang
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Kai-Wei Chang
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Wen-tau Yih
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Paper Details:
Month: November
Year: 2016
Location: Austin, Texas
Venue:
EMNLP |
SIG: SIGDAT
Citations
URL
Neural Math Word Problem Solver with Reinforcement Learning
Danqing Huang
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Jing Liu
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Chin-Yew Lin
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Jian Yin
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Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
Haoruo Peng
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Ming-Wei Chang
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Wen-tau Yih
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Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
Shyam Upadhyay
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Ming-Wei Chang
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Coreference Resolution on Math Problem Text in Japanese
Takumi Ito
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Takuya Matsuzaki
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Satoshi Sato
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Semantic Parsing of Pre-university Math Problems
Takuya Matsuzaki
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Takumi Ito
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Hidenao Iwane
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Hirokazu Anai
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Noriko H. Arai
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Using Intermediate Representations to Solve Math Word Problems
Danqing Huang
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Jin-Ge Yao
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Chin-Yew Lin
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Qingyu Zhou
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Jian Yin
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Numeracy for Language Models: Evaluating and Improving their Ability to Predict Numbers
Georgios Spithourakis
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Sebastian Riedel
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Mapping to Declarative Knowledge for Word Problem Solving
Subhro Roy
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Dan Roth
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https://aka.ms/dataimplicit
https://aka.ms/datadraw
http://www.algebra.com
https://aka.ms/datadraw
https://aka.ms/derivationpaper
Field Of Study
Task
Language Understanding
Semantic Parsing
Question Answering
Approach
Deep Learning
Structured Prediction
Language
English
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