no code implementations • CL (ACL) 2021 • Wenya Wang, Sinno Jialin Pan
Various deep neural networks have been proposed to jointly perform entity extraction and relation prediction, which only propagate information implicitly via representation learning.
no code implementations • EMNLP 2020 • Meixi Wu, Wenya Wang, Sinno Jialin Pan
Though deep learning has achieved significant success in various NLP tasks, most deep learning models lack the capability of encoding explicit domain knowledge to model complex causal relationships among different types of variables.
no code implementations • 11 Apr 2024 • Quanyu Long, Yin Wu, Wenya Wang, Sinno Jialin Pan
Counter-intuitively, we find that the demonstrations have a marginal impact on provoking discriminative knowledge of language models.
1 code implementation • 21 Feb 2024 • Quanyu Long, Yue Deng, Leilei Gan, Wenya Wang, Sinno Jialin Pan
To achieve this, we propose a perilous backdoor attack triggered by grammar errors in dense passage retrieval.
no code implementations • 20 Nov 2023 • Quanyu Long, Wenya Wang, Sinno Jialin Pan
Large language models (LLMs) have showcased their capability with few-shot inference known as in-context learning.
1 code implementation • 27 Oct 2023 • Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing
These findings underscore the challenging nature of the SOUL task for existing models, emphasizing the need for further advancements in sentiment analysis to address its complexities.
1 code implementation • 10 Oct 2023 • Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing
The experimental results reveal that in the unintentional scenario, the rate of unsafe content increases as the availability of languages decreases.
1 code implementation • 24 May 2023 • Wenxuan Zhang, Yue Deng, Bing Liu, Sinno Jialin Pan, Lidong Bing
This paper aims to provide a comprehensive investigation into the capabilities of LLMs in performing various sentiment analysis tasks, from conventional sentiment classification to aspect-based sentiment analysis and multifaceted analysis of subjective texts.
1 code implementation • 16 May 2023 • Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing
Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
no code implementations • ICCV 2023 • Haosen Shi, Shen Ren, Tianwei Zhang, Sinno Jialin Pan
A scheduling mechanism following the concept of curriculum learning is also designed to progressively change the sharing strategy to increase the level of sharing during the learning process.
1 code implementation • ICLR 2022 • Jianda Chen, Sinno Jialin Pan
How to learn an effective reinforcement learning-based model for control tasks from high-level visual observations is a practical and challenging problem.
1 code implementation • 16 Nov 2022 • Tianze Luo, Zhanfeng Mo, Sinno Jialin Pan
In this paper, we argue that running full-rank diffusion SDEs on the whole graph adjacency matrix space hinders diffusion models from learning graph topology generation, and hence significantly deteriorates the quality of generated graph data.
no code implementations • 29 Sep 2022 • Danni Peng, Sinno Jialin Pan
To address the distribution shifts between training and test data, domain generalization (DG) leverages multiple source domains to learn a model that generalizes well to unseen domains.
no code implementations • NAACL 2022 • Quanyu Long, Tianze Luo, Wenya Wang, Sinno Jialin Pan
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach.
no code implementations • 14 Jun 2022 • Wang Lu, Jindong Wang, Yiqiang Chen, Sinno Jialin Pan, Chunyu Hu, Xin Qin
Training on existing data often makes the model biased towards the distribution of the training data, thus the model might perform terribly on test data with different distributions.
1 code implementation • 8 Nov 2021 • Danni Peng, Sinno Jialin Pan, Jie Zhang, AnXiang Zeng
Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily.
1 code implementation • 7 Jul 2021 • Tianbo Li, Tianze Luo, Yiping Ke, Sinno Jialin Pan
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
no code implementations • NeurIPS 2020 • Jianda Chen, Shangyu Chen, Sinno Jialin Pan
In this paper, we propose a deep reinforcement learning (DRL) based framework to efficiently perform runtime channel pruning on convolutional neural networks (CNNs).
no code implementations • NeurIPS Workshop LMCA 2020 • Haiyan Yin, Yingzhen Li, Sinno Jialin Pan, Cheng Zhang, Sebastian Tschiatschek
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem.
no code implementations • 6 Dec 2019 • Wenya Wang, Sinno Jialin Pan
To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner.
no code implementations • CL 2019 • Wenya Wang, Sinno Jialin Pan
In this article, we explore the constructions of recursive neural networks based on the dependency tree of each sentence for associating syntactic structure with feature learning.
1 code implementation • NeurIPS 2019 • Shangyu Chen, Wenya Wang, Sinno Jialin Pan
However, these methods only heuristically make training-based quantization applicable, without further analysis on how the approximated gradients can assist training of a quantized network.
no code implementations • 11 Nov 2019 • Junyi Shen, Hankz Hankui Zhuo, Jin Xu, Bin Zhong, Sinno Jialin Pan
However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.
no code implementations • 25 Sep 2019 • Haiyan Yin, Jianda Chen, Sinno Jialin Pan
First, we propose a new reasoning paradigm to infer the novelty for the partially observable states, which is built upon forward dynamics prediction.
no code implementations • ACL 2018 • Wenya Wang, Sinno Jialin Pan
Fine-grained opinion analysis aims to extract aspect and opinion terms from each sentence for opinion summarization.
no code implementations • CVPR 2018 • Haoliang Li, Sinno Jialin Pan, Shiqi Wang, Alex C. Kot
In this paper, we tackle the problem of domain generalization: how to learn a generalized feature representation for an âunseenâ target domain by taking the advantage of multiple seen source-domain data.
Ranked #49 on Domain Generalization on PACS
no code implementations • 3 Jul 2017 • Haiyan Yin, Jianda Chen, Sinno Jialin Pan
In deep reinforcement learning (RL) tasks, an efficient exploration mechanism should be able to encourage an agent to take actions that lead to less frequent states which may yield higher accumulative future return.
2 code implementations • NeurIPS 2017 • Xin Dong, Shangyu Chen, Sinno Jialin Pan
How to develop slim and accurate deep neural networks has become crucial for real- world applications, especially for those employed in embedded systems.
no code implementations • AAAI 2017 • Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao
To achieve this task, one effective approach is to exploit relations between aspect terms and opinion terms by parsing syntactic structure for each sentence.
no code implementations • 6 Feb 2017 • Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier
This problem involves the identification of aspect and opinion terms within each sentence, as well as the categorization of the identified terms.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • 13 Dec 2016 • Sulin Liu, Sinno Jialin Pan, Qirong Ho
Due to heavy communication caused by transmitting the data and the issue of data privacy and security, it is impossible to send data of different task to a master machine to perform multi-task learning.
no code implementations • 13 May 2016 • Joey Tianyi Zhou, Xinxing Xu, Sinno Jialin Pan, Ivor W. Tsang, Zheng Qin, Rick Siow Mong Goh
Specifically, we extend the standard learning to hash method, Iterative Quantization (ITQ), in a transfer learning manner, namely ITQ+.
no code implementations • EMNLP 2016 • Wenya Wang, Sinno Jialin Pan, Daniel Dahlmeier, Xiaokui Xiao
Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA)