1 code implementation • ACL 2022 • Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
no code implementations • 30 May 2022 • Lu Yin, Vlado Menkovski, Meng Fang, Tianjin Huang, Yulong Pei, Mykola Pechenizkiy, Decebal Constantin Mocanu, Shiwei Liu
Recent works on sparse neural network training (sparse training) have shown that a compelling trade-off between performance and efficiency can be achieved by training intrinsically sparse neural networks from scratch.
1 code implementation • 22 May 2022 • Yibin Lei, Yu Cao, Dianqi Li, Tianyi Zhou, Meng Fang, Mykola Pechenizkiy
Generating high-quality textual adversarial examples is critical for investigating the pitfalls of natural language processing (NLP) models and further promoting their robustness.
1 code implementation • 21 Apr 2022 • Zihan Zhang, Meng Fang, Ling Chen, Mohammad-Reza Namazi-Rad
Recent work incorporates pre-trained word embeddings such as BERT embeddings into Neural Topic Models (NTMs), generating highly coherent topics.
1 code implementation • ACL 2022 • Yu Cao, Wei Bi, Meng Fang, Shuming Shi, DaCheng Tao
To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve its performance.
1 code implementation • ACL 2022 • Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
Text-based games provide an interactive way to study natural language processing.
1 code implementation • ICLR 2022 • Rui Yang, Yiming Lu, Wenzhe Li, Hao Sun, Meng Fang, Yali Du, Xiu Li, Lei Han, Chongjie Zhang
In this paper, we revisit the theoretical property of GCSL -- optimizing a lower bound of the goal reaching objective, and extend GCSL as a novel offline goal-conditioned RL algorithm.
no code implementations • 29 Sep 2021 • Meng Fang, Yunqiu Xu, Yali Du, Ling Chen, Chengqi Zhang
In a variety of text-based games, we show that this simple method results in competitive performance for agents.
no code implementations • 29 Sep 2021 • Zhihao Cheng, Li Shen, Meng Fang, Liu Liu, DaCheng Tao
Imitation Learning (IL) merely concentrates on reproducing expert behaviors and could take dangerous actions, which is unbearable in safety-critical scenarios.
1 code implementation • Findings (EMNLP) 2021 • Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang
Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
1 code implementation • Findings (EMNLP) 2021 • Iftitahu Ni'mah, Meng Fang, Vlado Menkovski, Mykola Pechenizkiy
The ability to detect Out-of-Domain (OOD) inputs has been a critical requirement in many real-world NLP applications.
no code implementations • 9 Jul 2021 • Hao Sun, Ziping Xu, Meng Fang, Zhenghao Peng, Jiadong Guo, Bo Dai, Bolei Zhou
Safe exploration is crucial for the real-world application of reinforcement learning (RL).
1 code implementation • 1 Jul 2021 • Rui Yang, Meng Fang, Lei Han, Yali Du, Feng Luo, Xiu Li
Replacing original goals with virtual goals generated from interaction with a trained dynamics model leads to a novel relabeling method, model-based relabeling (MBR).
1 code implementation • NAACL 2021 • Yinya Huang, Meng Fang, Yu Cao, LiWei Wang, Xiaodan Liang
The model encodes discourse information as a graph with elementary discourse units (EDUs) and discourse relations, and learns the discourse-aware features via a graph network for downstream QA tasks.
Ranked #17 on
Reading Comprehension
on ReClor
1 code implementation • 2 Mar 2021 • Yu Cao, Liang Ding, Zhiliang Tian, Meng Fang
Dialogue generation models face the challenge of producing generic and repetitive responses.
no code implementations • 1 Jan 2021 • Hao Sun, Ziping Xu, Meng Fang, Yuhang Song, Jiechao Xiong, Bo Dai, Zhengyou Zhang, Bolei Zhou
Despite the remarkable progress made by the policy gradient algorithms in reinforcement learning (RL), sub-optimal policies usually result from the local exploration property of the policy gradient update.
no code implementations • 1 Jan 2021 • Yali Du, Yifan Zhao, Meng Fang, Jun Wang, Gangyan Xu, Haifeng Zhang
Dealing with multi-agent control in networked systems is one of the biggest challenges in Reinforcement Learning (RL) and limited success has been presented compared to recent deep reinforcement learning in single-agent domain.
no code implementations • 24 Dec 2020 • Yinya Huang, Meng Fang, Xunlin Zhan, Qingxing Cao, Xiaodan Liang, Liang Lin
It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance.
1 code implementation • 27 Nov 2020 • Lei Han, Jiechao Xiong, Peng Sun, Xinghai Sun, Meng Fang, Qingwei Guo, Qiaobo Chen, Tengfei Shi, Hongsheng Yu, Xipeng Wu, Zhengyou Zhang
We show that with orders of less computation scale, a faithful reimplementation of AlphaStar's methods can not succeed and the proposed techniques are necessary to ensure TStarBot-X's competitive performance.
1 code implementation • 25 Nov 2020 • Peng Sun, Jiechao Xiong, Lei Han, Xinghai Sun, Shuxing Li, Jiawei Xu, Meng Fang, Zhengyou Zhang
This poses non-trivial difficulties for researchers or engineers and prevents the application of MARL to a broader range of real-world problems.
1 code implementation • NeurIPS 2020 • Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Joey Tianyi Zhou, Chengqi Zhang
We study reinforcement learning (RL) for text-based games, which are interactive simulations in the context of natural language.
no code implementations • 16 Oct 2020 • Zhihao Cheng, Liu Liu, Aishan Liu, Hao Sun, Meng Fang, DaCheng Tao
By contrast, this paper proves that LfO is almost equivalent to LfD in the deterministic robot environment, and more generally even in the robot environment with bounded randomness.
1 code implementation • Findings of the Association for Computational Linguistics 2020 • Yu Cao, Wei Bi, Meng Fang, DaCheng Tao
In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2.
no code implementations • 11 Jun 2020 • Hao Sun, Ziping Xu, Yuhang Song, Meng Fang, Jiechao Xiong, Bo Dai, Bolei Zhou
However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limited sample efficiency.
1 code implementation • NeurIPS 2019 • Yali Du, Lei Han, Meng Fang, Ji Liu, Tianhong Dai, DaCheng Tao
A great challenge in cooperative decentralized multi-agent reinforcement learning (MARL) is generating diversified behaviors for each individual agent when receiving only a team reward.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
1 code implementation • NeurIPS 2019 • Meng Fang, Tianyi Zhou, Yali Du, Lei Han, Zhengyou Zhang
This ``Goal-and-Curiosity-driven Curriculum Learning'' leads to ``Curriculum-guided HER (CHER)'', which adaptively and dynamically controls the exploration-exploitation trade-off during the learning process via hindsight experience selection.
1 code implementation • 13 Nov 2019 • Yu Cao, Meng Fang, Baosheng Yu, Joey Tianyi Zhou
On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains.
1 code implementation • 10 Sep 2019 • Yutai Hou, Meng Fang, Wanxiang Che, Ting Liu
The framework builds a user simulator by first generating diverse dialogue data from templates and then build a new State2Seq user simulator on the data.
1 code implementation • 26 Jul 2019 • Tingguang Li, Weitao Xi, Meng Fang, Jia Xu, Max Qing-Hu Meng
We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand.
Robotics
2 code implementations • 20 Jul 2019 • Qing Wang, Jiechao Xiong, Lei Han, Meng Fang, Xinghai Sun, Zhuobin Zheng, Peng Sun, Zhengyou Zhang
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research.
no code implementations • 6 Jul 2019 • Xiaomeng Li, Lequan Yu, Chi-Wing Fu, Meng Fang, Pheng-Ann Heng
However, the importance of feature embedding, i. e., exploring the relationship among training samples, is neglected.
no code implementations • ACL 2019 • Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, Kenneth Kwok
We propose a new neural transfer method termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER).
1 code implementation • ICLR 2019 • Meng Fang, Cheng Zhou, Bei Shi, Boqing Gong, Jia Xu, Tong Zhang
Dealing with sparse rewards is one of the most important challenges in reinforcement learning (RL), especially when a goal is dynamic (e. g., to grasp a moving object).
1 code implementation • NAACL 2019 • Yu Cao, Meng Fang, DaCheng Tao
Graph convolutional networks are used to obtain a relation-aware representation of nodes for entity graphs built from documents with multi-level features.
1 code implementation • 9 Sep 2018 • Yali Du, Meng Fang, Jin-Feng Yi, Jun Cheng, DaCheng Tao
First, we initialize an adversarial example with a gray color image on which every pixel has roughly the same importance for the target model.
1 code implementation • 2 May 2018 • Songyou Peng, Le Zhang, Yutong Ban, Meng Fang, Stefan Winkler
In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.
1 code implementation • EMNLP 2017 • Meng Fang, Yuan Li, Trevor Cohn
Active learning aims to select a small subset of data for annotation such that a classifier learned on the data is highly accurate.
1 code implementation • ACL 2017 • Meng Fang, Trevor Cohn
Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora.
no code implementations • 19 Aug 2016 • Yang Wang, Wenjie Zhang, Lin Wu, Xuemin Lin, Meng Fang, Shirui Pan
Multi-view spectral clustering, which aims at yielding an agreement or consensus data objects grouping across multi-views with their graph laplacian matrices, is a fundamental clustering problem.
no code implementations • CONLL 2016 • Meng Fang, Trevor Cohn
Cross lingual projection of linguistic annotation suffers from many sources of bias and noise, leading to unreliable annotations that cannot be used directly.
no code implementations • CVPR 2015 • Chen Gong, DaCheng Tao, Wei Liu, Stephen J. Maybank, Meng Fang, Keren Fu, Jie Yang
In the teaching-to-learn step, a teacher is designed to arrange the regions from simple to difficult and then assign the simplest regions to the learner.
no code implementations • 11 Mar 2014 • Meng Fang, Jie Yin, Xingquan Zhu
In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks.