Mapping natural-language problems to formal-language solutions using structured neural representations
By Kezhen Chen, Qiuyuan Huang, Hamid Palangi, Paul Smolensky, Kenneth D. Forbus, Jianfeng Gao. 2019
28 citations recorded by the source corpus.
Subfields: Natural Language Processing
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
- William Yang Wang
- Heewoo Jun
- Mark Chen
- Vineet Kosaraju
- Ting-Hao 'Kenneth' Huang
- Jacob Hilton
- Jerry Tworek
- Mo Bavarian
Citations in the corpus
- Training Verifiers to Solve Math Word Problems (2021, methodology): “…work has improved performance by designing specialized encoder-decoder architectures (Amini et al., 2019; Chiang and Chen, 2018; Xie and Sun, 2019; Chen et al., 2020; Li et al., 2020), with the strongest results often relying on large pretrained encoders from the BERT family (Chen et al., 2019;…”
- FinQA: A Dataset of Numerical Reasoning over Financial Data (2021, methodology): “As most existing QA datasets were constructed by MTurk workers (Yang et al., 2018; Dua et al., 2019; Chen et al., 2020c), it requires substantial domain-specific knowledge to compose meaningful questions that are hard for computers to answer.”
- Learning Inference Rules with Neural TP-Reasoner (2020, unknown): “TPR ‘binding’ and ‘unbinding’ operations have been used in natural-language tasks(Palangi et al., 2018; Huang et al., 2018, 2019; Kezhen et al., 2020).”