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Shen Wang 0005

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2 papers

1 author row

ICLR Conference 2023 Conference Paper

STREET: A Multi-Task Structured Reasoning and Explanation Benchmark

Danilo Neves Ribeiro
Shen Wang 0005
Xiaofei Ma 0001
Henghui Zhu
Rui Dong
Deguang Kong
Juliette Burger
Anjelica Ramos

We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark. Unlike most existing question-answering (QA) datasets, we expect models to not only answer questions, but also produce step-by-step structured explanations describing how premises in the question are used to produce intermediate conclusions that can prove the correctness of a certain answer. We perform extensive evaluation with popular language models such as few-shot prompting GPT-3 and fine-tuned T5. We find that these models still lag behind human performance when producing such structured reasoning steps. We believe this work will provide a way for the community to better train and test systems on multi-step reasoning and explanations in natural language.

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ICML Conference 2017 Conference Paper

Kernelized Support Tensor Machines

Lifang He 0001
Chun-Ta Lu
Guixiang Ma
Shen Wang 0005
LinLin Shen
Philip S. Yu
Ann B. Ragin

In the context of supervised tensor learning, preserving the structural information and exploiting the discriminative nonlinear relationships of tensor data are crucial for improving the performance of learning tasks. Based on tensor factorization theory and kernel methods, we propose a novel Kernelized Support Tensor Machine (KSTM) which integrates kernelized tensor factorization with maximum-margin criterion. Specifically, the kernelized factorization technique is introduced to approximate the tensor data in kernel space such that the complex nonlinear relationships within tensor data can be explored. Further, dual structural preserving kernels are devised to learn the nonlinear boundary between tensor data. As a result of joint optimization, the kernels obtained in KSTM exhibit better generalization power to discriminative analysis. The experimental results on real-world neuroimaging datasets show the superiority of KSTM over the state-of-the-art techniques.

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