基于HowNet的无监督汉语动词隐喻识别方法(Unsupervised Chinese Verb Metaphor Recognition Method Based on HowNet)

no code implementations • CCL 2021 • Minghao Zhang, Dongyu Zhang, Hongfei Lin

“隐喻是人类思维和语言理解的核心问题。随着互联网发展和海量文本出现, 利用自然语言处理技术对隐喻文本进行自动识别成为一种迫切的需求。但是目前在汉语隐喻识别研究中, 由于语义资源有限, 导致模型容易过拟合。此外, 主流隐喻识别方法存在可解释性差的缺点。针对上述问题, 本文构建了一个规模较大的汉语动词隐喻数据集, 并且提出了一种基于HowNet的无监督汉语动词隐喻识别模型。实验结果表明, 本文提出的模型能够有效地应用于动词隐喻识别任务, 识别效果超过了对比的无监督模型;并且, 与其它用于隐喻识别的深度学习模型相比, 本文模型具有结构简单、参数少、可解释性强的优点。”

Vision-Guided Quadrupedal Locomotion in the Wild with Multi-Modal Delay Randomization

1 code implementation • 29 Sep 2021 • Chieko Sarah Imai, Minghao Zhang, Yuchen Zhang, Marcin Kierebinski, Ruihan Yang, Yuzhe Qin, Xiaolong Wang

While Reinforcement Learning (RL) provides a promising paradigm for agile locomotion skills with vision inputs in simulation, it is still very challenging to deploy the RL policy in the real world.

Reinforcement Learning (RL)

MultiMET: A Multimodal Dataset for Metaphor Understanding

no code implementations • ACL 2021 • Dongyu Zhang, Minghao Zhang, Heting Zhang, Liang Yang, Hongfei Lin

Metaphor involves not only a linguistic phenomenon, but also a cognitive phenomenon structuring human thought, which makes understanding it challenging.

Tianshou: a Highly Modularized Deep Reinforcement Learning Library

1 code implementation • 29 Jul 2021 • Jiayi Weng, Huayu Chen, Dong Yan, Kaichao You, Alexis Duburcq, Minghao Zhang, Yi Su, Hang Su, Jun Zhu

In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.

reinforcement-learning Reinforcement Learning (RL)

Learning Vision-Guided Quadrupedal Locomotion End-to-End with Cross-Modal Transformers

1 code implementation • ICLR 2022 • Ruihan Yang, Minghao Zhang, Nicklas Hansen, Huazhe Xu, Xiaolong Wang

Our key insight is that proprioceptive states only offer contact measurements for immediate reaction, whereas an agent equipped with visual sensory observations can learn to proactively maneuver environments with obstacles and uneven terrain by anticipating changes in the environment many steps ahead.

Reinforcement Learning (RL)

DAIR: Disentangled Attention Intrinsic Regularization for Safe and Efficient Bimanual Manipulation

no code implementations • 10 Jun 2021 • Minghao Zhang, Pingcheng Jian, Yi Wu, Huazhe Xu, Xiaolong Wang

We address the problem of safely solving complex bimanual robot manipulation tasks with sparse rewards.

Robot Manipulation

Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning

1 code implementation • NeurIPS 2020 • Guangxiang Zhu, Minghao Zhang, Honglak Lee, Chongjie Zhang

It maximizes the mutual information between imaginary and real trajectories so that the policy improvement learned from imaginary trajectories can be easily generalized to real trajectories.

Model-based Reinforcement Learning reinforcement-learning +1