DRAW: A Challenging and Diverse Algebra Word Problem Set

By Shyam Upadhyay, Ming-Wei Chang. 2015

20 citations recorded by the source corpus.

Subfields: Natural Language Processing, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Natural Language Processing

1. Hannaneh Hajishirzi
2. Lei Wang
3. Jian Yin
4. Rik Koncel-Kedziorski
5. Shuming Shi
6. Danqing Huang
7. Chin-Yew Lin
8. Dongxiang Zhang

In Evaluation & Benchmarks

1. Hannaneh Hajishirzi
2. Shuming Shi
3. Jian Yin
4. Danqing Huang
5. Chin-Yew Lin
6. Rik Koncel-Kedziorski
7. Pan Lu
8. Tony Xia

Citations in the corpus

• A Diverse Corpus for Evaluating and Developing English Math Word Problem Solvers (2020, background): “For AI2, IL, KAZB, ALGES, AllArith, DRAW, and MathQA, we follow their own either n-fold cross-validation setting or the train/dev/test splits originally specified in each dataset.”
• How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation (2016, background): “As mentioned in (Upadhyay and Chang, 2015), Alg514 has a skewed problem distribution, with a few templates covering almost 50% problems.”
• TheoremQA: A Theorem-driven Question Answering dataset (2023, background): “DRAW (Upadhyay and Chang, 2015) Algebra Elementary School Generated MAWPS (Koncel-Kedziorski et al.”
• Data-Driven Methods for Solving Algebra Word Problems (2018, methodology): “Datasets For comparison, we report solution accuracy on the Chinese language Math23K dataset (Wang et al., 2017), and the English language D RAW (Upadhyay and Chang, 2015) and M AWPS (Koncel-Kedziorski et al., 2016) datasets.”