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Converse: A Tree-Based Modular Task-Oriented Dialogue System


Mar 30, 2022
Tian Xie, Xinyi Yang, Angela S. Lin, Feihong Wu, Kazuma Hashimoto, Jin Qu, Young Mo Kang, Wenpeng Yin, Huan Wang, Semih Yavuz, Gang Wu, Michael Jones, Richard Socher, Yingbo Zhou, Wenhao Liu, Caiming Xiong


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The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning


Aug 05, 2021
Stephan Zheng, Alexander Trott, Sunil Srinivasa, David C. Parkes, Richard Socher

* Substantial Extension of https://arxiv.org/abs/2004.13332. SZ and AT contributed equally 

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Evaluating State-of-the-Art Classification Models Against Bayes Optimality


Jun 07, 2021
Ryan Theisen, Huan Wang, Lav R. Varshney, Caiming Xiong, Richard Socher


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Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing


Dec 31, 2020
Xi Victoria Lin, Richard Socher, Caiming Xiong

* EMNLP Findings 2020 long paper extended; 23 pages 

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Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization


Dec 28, 2020
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand, Giancarlo Kerg, Huan Wang, Caiming Xiong, Richard Socher, Kyunghyun Cho, Krzysztof Geras

* The last two authors contributed equally 

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Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference


Oct 25, 2020
Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu, Chien-Sheng Wu, Yao Wan, Philip S. Yu, Richard Socher, Caiming Xiong

* 19 pages, accepted by EMNLP 2020 main conference as a long paper. Code will be available at https://github.com/salesforce/DNNC-few-shot-intent 

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Online Structured Meta-learning


Oct 22, 2020
Huaxiu Yao, Yingbo Zhou, Mehrdad Mahdavi, Zhenhui Li, Richard Socher, Caiming Xiong

* Accepted by NeurIPS 2020 

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Explaining and Improving Model Behavior with k Nearest Neighbor Representations


Oct 18, 2020
Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin, Tong Niu, Richard Socher, Caiming Xiong


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Explaining Creative Artifacts


Oct 14, 2020
Lav R. Varshney, Nazneen Fatema Rajani, Richard Socher

* 2020 Workshop on Human Interpretability in Machine Learning (WHI), at ICML 2020 

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