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.
Ranked #1 on
Argument Pair Extraction (APE)
on RR
no code implementations • COLING 2022 • Zijie Lin, Bin Liang, Yunfei Long, Yixue Dang, Min Yang, Min Zhang, Ruifeng Xu
This essentially allows the framework to understand the appropriate graph structures for learning intricate relations among different modalities.
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。”
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。”
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.
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.
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。
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.
1 code implementation • 19 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.
no code implementations • 3 Feb 2023 • Chu Wang, Manfeng Dou, Zhongliang Li, Rachid Outbib, Dongdong Zhao, Jian Zuo, Yuanlin Wang, Bin Liang, Peng Wang
Data-centric prognostics is beneficial to improve the reliability and safety of proton exchange membrane fuel cell (PEMFC).
1 code implementation • 8 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.
1 code implementation • 14 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.
no code implementations • 1 Dec 2022 • Qiyue Yin, Tongtong Yu, Shengqi Shen, Jun Yang, Meijing Zhao, Kaiqi Huang, Bin Liang, Liang Wang
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems.
no code implementations • 1 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.
no code implementations • 16 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.
no code implementations • 1 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.
no code implementations • 13 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).
no code implementations • 15 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.
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.
1 code implementation • 1 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.
no code implementations • 9 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.
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.
no code implementations • 5 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.
no code implementations • 16 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.
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).
2 code implementations • 23 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.
no code implementations • 26 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.