Search Results for author: Bin Liang

Found 28 papers, 10 papers with code

Argument Pair Extraction with Mutual Guidance and Inter-sentence Relation Graph

no code implementations EMNLP 2021 Jianzhu Bao, Bin Liang, Jingyi Sun, Yice Zhang, Min Yang, Ruifeng Xu

In this paper, we tackle the APE task by a mutual guidance framework, which could utilize the information of an argument in one passage to guide the identification of arguments that can form pairs with it in another passage.

Argument Pair Extraction (APE) Relation Classification

面向话题的讽刺识别:新任务、新数据和新方法(Topic-Oriented Sarcasm Detection: New Task, New Dataset and New Method)

no code implementations CCL 2022 Bin Liang, Zijie Lin, Bing Qin, Ruifeng Xu

“现有的文本讽刺识别研究通常只停留在句子级别的讽刺表达分类, 缺乏考虑讽刺对象对讽刺表达的影响。针对这一问题, 本文提出一个新的面向话题的讽刺识别任务。该任务通过话题的引入, 以话题作为讽刺对象, 有助于更好地理解和建模讽刺表达。对应地, 本文构建了一个新的面向话题的讽刺识别数据集。这个数据集包含了707个话题, 以及对应的4871个话题-评论对组。在此基础上, 基于提示学习和大规模预训练语言模型, 提出了一种面向话题的讽刺表达提示学习模型。在本文构建的面向话题讽刺识别数据集上的实验结果表明, 相比基线模型, 本文所提出的面向话题的讽刺表达提示学习模型取得了更优的性能。同时, 实验分析也表明本文提出的面向话题的讽刺识别任务相比传统的句子级讽刺识别任务更具挑战性。”

Sarcasm Detection

基于主题提示学习的零样本立场检测方法(A Topic-based Prompt Learning Method for Zero-Shot Stance Detection)

no code implementations CCL 2022 Zixiao Chen, Bin Liang, Ruifeng Xu

“零样本立场检测目的是针对未知目标数据进行立场极性预测。一般而言, 文本的立场表达是与所讨论的目标主题是紧密联系的。针对未知目标的立场检测, 本文将立场表达划分为两种类型:一类在说话者面向不同的主题和讨论目标时表达相同的立场态度, 称之为目标无关的表达;另一类在说话者面向特定主题和讨论目标时才表达相应的立场态度, 本文称之为目标依赖的表达。对这两种表达进行区分, 有效学习到目标无关的表达方式并忽略目标依赖的表达方式, 有望强化模型的可迁移能力, 使其更加适应零样本立场检测任务。据此, 本文提出了一种基于主题提示学习的零样本立场检测方法。具体而言, 受自监督学习的启发, 本文为了零样本立场检测设置了一个代理任务框架。其中, 代理任务通过掩盖上下文中的目标主题词生成辅助样本, 并基于提示学习分别预测原样本和辅助样本的立场表达, 随后判断原样本和辅助样本的立场表达是否一致, 从而在无需人工标注的情况下判断样本的立场表达是否依赖于目标的代理标签。然后, 将此代理标签提供给立场检测模型, 对应学习可迁移的立场检测特征。在两个基准数据集上的大量实验表明, 本文提出的方法在零样本立场检测任务中相比基线模型取得了更优的性能。”

Stance Detection

Multi-Modal Sarcasm Detection via Cross-Modal Graph Convolutional Network

1 code implementation ACL 2022 Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu

Then, the descriptions of the objects are served as a bridge to determine the importance of the association between the objects of image modality and the contextual words of text modality, so as to build a cross-modal graph for each multi-modal instance.

Sarcasm Detection

JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection

1 code implementation ACL 2022 Bin Liang, Qinglin Zhu, Xiang Li, Min Yang, Lin Gui, Yulan He, Ruifeng Xu

In this paper, we propose a joint contrastive learning (JointCL) framework, which consists of stance contrastive learning and target-aware prototypical graph contrastive learning.

Contrastive Learning Stance Detection

基于循环交互注意力网络的问答立场分析(A Recurrent Interactive Attention Network for Answer Stance Analysis)

no code implementations CCL 2020 Wangda Luo, YuHan Liu, Bin Liang, Ruifeng Xu

针对问答立场任务中, 现有方法难以提取问答文本间的依赖关系问题, 本文提出一种基于循环交互注意力(Recurrent Interactive Attention, RIA)网络的问答立场分析方法。该方法通过模仿人类阅读理解时的思维方式, 基于交互注意力机制和循环迭代方法, 有效地从问题和答案的相互联系中挖掘问答文本的立场信息。此外, 该方法将问题进行陈述化表示, 有效地解决疑问句表述下问题文本无法明确表达自身立场的问题。实验结果表明, 本文方法取得了比现有模型方法更好的效果, 同时证明该方法能有效拟合问答立场分析任务中的问答对依赖关系。

Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge

2 code implementations EMNLP 2021 Bin Liang, Hang Su, Rongdi Yin, Lin Gui, Min Yang, Qin Zhao, Xiaoqi Yu, Ruifeng Xu

To be specific, we first regard each aspect as a pivot to derive aspect-aware words that are highly related to the aspect from external affective commonsense knowledge.

graph construction Sentiment Analysis

Learning Diverse Risk Preferences in Population-based Self-play

1 code implementation19 May 2023 Yuhua Jiang, Qihan Liu, Xiaoteng Ma, Chenghao Li, Yiqin Yang, Jun Yang, Bin Liang, Qianchuan Zhao

In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.

reinforcement-learning Reinforcement Learning (RL)

Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention

1 code implementation8 Jan 2023 Kai Mo, Chongkun Xia, Xueqian Wang, Yuhong Deng, Xuehai Gao, Bin Liang

Foldformer can complete multi-step cloth manipulation tasks even when configurations of the cloth (e. g., size and pose) vary from configurations in the general demonstrations.

Domain Generalization by Learning and Removing Domain-specific Features

1 code implementation14 Dec 2022 Yu Ding, Lei Wang, Bin Liang, Shuming Liang, Yang Wang, Fang Chen

With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification.

Domain Generalization Image Classification

Dynamics-Adaptive Continual Reinforcement Learning via Progressive Contextualization

no code implementations1 Sep 2022 Tiantian Zhang, Zichuan Lin, Yuxing Wang, Deheng Ye, Qiang Fu, Wei Yang, Xueqian Wang, Bin Liang, Bo Yuan, Xiu Li

A key challenge of continual reinforcement learning (CRL) in dynamic environments is to promptly adapt the RL agent's behavior as the environment changes over its lifetime, while minimizing the catastrophic forgetting of the learned information.

Bayesian Inference Knowledge Distillation +3

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL

no code implementations16 Mar 2022 Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, Chongjie Zhang

Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.

Continuous Control Offline RL +2

A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning

no code implementations1 Jan 2022 Yuxing Wang, Tiantian Zhang, Yongzhe Chang, Bin Liang, Xueqian Wang, Bo Yuan

The integration of Reinforcement Learning (RL) and Evolutionary Algorithms (EAs) aims at simultaneously exploiting the sample efficiency as well as the diversity and robustness of the two paradigms.

Continuous Control OpenAI Gym +2

Probability Density Estimation Based Imitation Learning

no code implementations13 Dec 2021 Yang Liu, Yongzhe Chang, Shilei Jiang, Xueqian Wang, Bin Liang, Bo Yuan

In general, IL methods can be categorized into Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL).

Density Estimation Imitation Learning

AI in Human-computer Gaming: Techniques, Challenges and Opportunities

no code implementations15 Nov 2021 Qiyue Yin, Jun Yang, Kaiqi Huang, Meijing Zhao, Wancheng Ni, Bin Liang, Yan Huang, Shu Wu, Liang Wang

Through this survey, we 1) compare the main difficulties among different kinds of games and the corresponding techniques utilized for achieving professional human level AIs; 2) summarize the mainstream frameworks and techniques that can be properly relied on for developing AIs for complex human-computer gaming; 3) raise the challenges or drawbacks of current techniques in the successful AIs; and 4) try to point out future trends in human-computer gaming AIs.

Decision Making

Offline Reinforcement Learning with Value-based Episodic Memory

1 code implementation ICLR 2022 Xiaoteng Ma, Yiqin Yang, Hao Hu, Qihan Liu, Jun Yang, Chongjie Zhang, Qianchuan Zhao, Bin Liang

Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data.

D4RL Offline RL +2

Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation

1 code implementation1 Sep 2021 Tiantian Zhang, Xueqian Wang, Bin Liang, Bo Yuan

In this paper, we present IQ, i. e., interference-aware deep Q-learning, to mitigate catastrophic interference in single-task deep reinforcement learning.

General Reinforcement Learning Knowledge Distillation +5

We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature

no code implementations9 Jun 2021 Bin Liang, Jiachun Li, Jianjun Huang

Recently, the object detection based on deep learning has proven to be vulnerable to adversarial patch attacks.

object-detection Object Detection

Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment Analysis

1 code implementation COLING 2020 Bin Liang, Rongdi Yin, Lin Gui, Jiachen Du, Ruifeng Xu

Besides, to interactively extract the inter-aspect relations for the specific aspect, an inter-aspect GCN is adopted to model the representations learned by aspect-focused GCN based on the inter-aspect graph which is constructed by the relative dependencies between the aspect words and other aspects.

Sentiment Analysis

Utilizing machine learning to prevent water main breaks by understanding pipeline failure drivers

no code implementations5 Jun 2020 Dilusha Weeraddana, Bin Liang, Zhidong Li, Yang Wang, Fang Chen, Livia Bonazzi, Dean Phillips, Nitin Saxena

Data61 and Western Water worked collaboratively to apply engineering expertise and Machine Learning tools to find a cost-effective solution to the pipe failure problem in the region west of Melbourne, where on average 400 water main failures occur per year.

BIG-bench Machine Learning

Meta-neural-network for Realtime and Passive Deep-learning-based Object Recognition

no code implementations16 Sep 2019 Jingkai Weng, Yujiang Ding, Chengbo Hu, Xue-Feng Zhu, Bin Liang, Jing Yang, Jianchun Cheng

Deep-learning recently show great success across disciplines yet conventionally require time-consuming computer processing or bulky-sized diffractive elements.

Handwritten Digit Recognition Object Recognition

Context-aware Embedding for Targeted Aspect-based Sentiment Analysis

no code implementations ACL 2019 Bin Liang, Jiachen Du, Ruifeng Xu, Binyang Li, Hejiao Huang

Attention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA).

Aspect-Based Sentiment Analysis (ABSA)

Detecting Adversarial Image Examples in Deep Networks with Adaptive Noise Reduction

2 code implementations23 May 2017 Bin Liang, Hongcheng Li, Miaoqiang Su, Xirong Li, Wenchang Shi, Xiao-Feng Wang

Consequently, the adversarial example can be effectively detected by comparing the classification results of a given sample and its denoised version, without referring to any prior knowledge of attacks.


Deep Text Classification Can be Fooled

no code implementations26 Apr 2017 Bin Liang, Hongcheng Li, Miaoqiang Su, Pan Bian, Xirong Li, Wenchang Shi

In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack.

General Classification text-classification +1

Cannot find the paper you are looking for? You can Submit a new open access paper.