no code implementations • 28 Apr 2023 • Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Fei Wu, Kun Kuang
In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE.
1 code implementation • 21 Apr 2023 • Archiki Prasad, Swarnadeep Saha, Xiang Zhou, Mohit Bansal
Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them.
no code implementations • 20 Apr 2023 • Tonghua Su, Fuxiang Yang, Xiang Zhou, Donglin Di, Zhongjie Wang, Songze Li
Specifically, QuadNet consists of four parts, namely background inpainting, style encoder, content encoder, and fusion generator.
no code implementations • 4 Apr 2023 • Norman P. Jouppi, George Kurian, Sheng Li, Peter Ma, Rahul Nagarajan, Lifeng Nai, Nishant Patil, Suvinay Subramanian, Andy Swing, Brian Towles, Cliff Young, Xiang Zhou, Zongwei Zhou, David Patterson
For similar sized systems, it is ~4. 3x-4. 5x faster than the Graphcore IPU Bow and is 1. 2x-1. 7x faster and uses 1. 3x-1. 9x less power than the Nvidia A100.
no code implementations • 13 Feb 2023 • Xiang Zhou, Yuan Zeng, Yi Gong
Recent advances in deep generative adversarial networks (GAN) and self-attention mechanism have led to significant improvements in the challenging task of inpainting large missing regions in an image.
1 code implementation • 28 Nov 2022 • Yichen Jiang, Xiang Zhou, Mohit Bansal
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models.
no code implementations • 9 Sep 2022 • Jiayue Han, Zhiqiang Cai, Zhiyou Wu, Xiang Zhou
Thus, we propose the Residual-Quantile Adjustment (RQA) method for a better weight choice for each training sample.
1 code implementation • NAACL 2022 • Xiang Zhou, Shiyue Zhang, Mohit Bansal
MPoSM can model arbitrary tag dependency and perform POS induction through the objective of masked POS reconstruction.
1 code implementation • Findings (ACL) 2022 • Cenyuan Zhang, Xiang Zhou, Yixin Wan, Xiaoqing Zheng, Kai-Wei Chang, Cho-Jui Hsieh
Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models.
1 code implementation • 14 Mar 2022 • Archiki Prasad, Peter Hase, Xiang Zhou, Mohit Bansal
Providing natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting.
2 code implementations • 4 Mar 2022 • Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun
Combinatorial optimization is a well-established area in operations research and computer science.
no code implementations • 22 Jan 2022 • Simon J. Ward, Tengfei Cao, Xiang Zhou, Catie Chang, Sharon M. Weiss
Biosensors are an essential tool for medical diagnostics, environmental monitoring and food safety.
no code implementations • 19 Nov 2021 • Zhiqiang Cai, Ling Lin, Xiang Zhou
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution.
no code implementations • 10 Aug 2021 • Shuting Gu, Hongqiao Wang, Xiang Zhou
To reduce the number of expensive computations of the true gradients, we propose an active learning framework consisting of a statistical surrogate model, Gaussian process regression (GPR) for the energy function, and a single-walker dynamics method, gentle accent dynamics (GAD), for the saddle-type transition states.
no code implementations • 8 Jul 2021 • Yi Xiong, Ningyuan Chen, Xuefeng Gao, Xiang Zhou
We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs).
no code implementations • EACL 2021 • Xiang Zhou, Heba Elfardy, Christos Christodoulopoulos, Thomas Butler, Mohit Bansal
Using the observations and experimental results, we provide practical suggestions on how to create more reliable datasets for the unreliable news detection task.
1 code implementation • Findings (ACL) 2022 • Xiang Zhou, Yixin Nie, Mohit Bansal
We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference.
1 code implementation • EMNLP 2020 • Yixin Nie, Xiang Zhou, Mohit Bansal
Analysis reveals that: (1) high human disagreement exists in a noticeable amount of examples in these datasets; (2) the state-of-the-art models lack the ability to recover the distribution over human labels; (3) models achieve near-perfect accuracy on the subset of data with a high level of human agreement, whereas they can barely beat a random guess on the data with low levels of human agreement, which compose most of the common errors made by state-of-the-art models on the evaluation sets.
no code implementations • 22 Sep 2020 • Zhi Chen, Lu Chen, Xiang Zhou, Kai Yu
To the best of our knowledge, this is the first effort to optimize the DST module within DRL framework for on-line task-oriented spoken dialogue systems.
1 code implementation • 14 Sep 2020 • Longxiang Liu, Zhuosheng Zhang, Hai Zhao, Xi Zhou, Xiang Zhou
A multi-turn dialogue is composed of multiple utterances from two or more different speaker roles.
no code implementations • 14 Sep 2020 • Zhuosheng Zhang, Yiqing Zhang, Hai Zhao, Xi Zhou, Xiang Zhou
This paper presents a novel method to generate answers for non-extraction machine reading comprehension (MRC) tasks whose answers cannot be simply extracted as one span from the given passages.
no code implementations • 10 Jun 2020 • Alain Bensoussan, Yiqun Li, Dinh Phan Cao Nguyen, Minh-Binh Tran, Sheung Chi Phillip Yam, Xiang Zhou
Conversely Machine Learning can be used to solve large control problems.
1 code implementation • ACL 2020 • Xiang Zhou, Mohit Bansal
While deep learning models are making fast progress on the task of Natural Language Inference, recent studies have also shown that these models achieve high accuracy by exploiting several dataset biases, and without deep understanding of the language semantics.
1 code implementation • EMNLP 2020 • Xiang Zhou, Yixin Nie, Hao Tan, Mohit Bansal
For the first question, we conduct a thorough empirical study over analysis sets and find that in addition to the unstable final performance, the instability exists all along the training curve.
no code implementations • 7 Mar 2020 • Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, Qingyun Sun
In this work, we put different variants of stochastic ADMM into a unified form, which includes standard, linearized and gradient-based ADMM with relaxation, and study their dynamics via a continuous-time model approach.
no code implementations • NeurIPS 2021 • Xiang Zhou, Yi Xiong, Ningyuan Chen, Xuefeng Gao
We study a multi-armed bandit problem where the rewards exhibit regime switching.
1 code implementation • 25 Nov 2019 • Yitong Yan, Chuangchuang Liu, Changyou Chen, Xianfang Sun, Longcun Jin, Xiang Zhou
Firstly, instead of producing a single score to discriminate images between real and fake, we propose a variant, called Fine-grained Attention Generative Adversarial Network for image super-resolution (FASRGAN), to discriminate each pixel between real and fake.
1 code implementation • 5 Sep 2019 • Zhuosheng Zhang, Yuwei Wu, Hai Zhao, Zuchao Li, Shuailiang Zhang, Xi Zhou, Xiang Zhou
The latest work on language representations carefully integrates contextualized features into language model training, which enables a series of success especially in various machine reading comprehension and natural language inference tasks.
Ranked #6 on
Natural Language Inference
on SNLI
2 code implementations • 30 Aug 2019 • Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Multi-choice reading comprehension is a challenging task to select an answer from a set of candidate options when given passage and question.
no code implementations • 27 Jan 2019 • Shuailiang Zhang, Hai Zhao, Yuwei Wu, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Multi-choice reading comprehension is a challenging task that requires complex reasoning procedure.
Ranked #3 on
Question Answering
on RACE
1 code implementation • 16 Jan 2019 • Zuchao Li, Shexia He, Hai Zhao, Yiqing Zhang, Zhuosheng Zhang, Xi Zhou, Xiang Zhou
Semantic role labeling (SRL) aims to discover the predicateargument structure of a sentence.
Ranked #9 on
Semantic Role Labeling
on CoNLL 2005
1 code implementation • 12 Mar 2018 • Zilong Tan, Kimberly Roche, Xiang Zhou, Sayan Mukherjee
We provide theoretical guarantees for our learning algorithms, demonstrating the robustness of parameter estimation.
no code implementations • EMNLP 2017 • Lu Chen, Xiang Zhou, Cheng Chang, Runzhe Yang, Kai Yu
Hand-crafted rules and reinforcement learning (RL) are two popular choices to obtain dialogue policy.
no code implementations • EMNLP 2017 • Cheng Chang, Runzhe Yang, Lu Chen, Xiang Zhou, Kai Yu
The key to building an evolvable dialogue system in real-world scenarios is to ensure an affordable on-line dialogue policy learning, which requires the on-line learning process to be safe, efficient and economical.
no code implementations • EACL 2017 • Lu Chen, Runzhe Yang, Cheng Chang, Zihao Ye, Xiang Zhou, Kai Yu
On-line dialogue policy learning is the key for building evolvable conversational agent in real world scenarios.
1 code implementation • 5 Aug 2015 • Lorin Crawford, Kris C. Wood, Xiang Zhou, Sayan Mukherjee
State-of-the-art methods for genomic selection and association mapping are based on kernel regression and linear models, respectively.