no code implementations • Findings (ACL) 2022 • Yinan Bao, Qianwen Ma, Lingwei Wei, Wei Zhou, Songlin Hu
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
no code implementations • COLING 2022 • Lingwei Wei, Dou Hu, Yantong Lai, Wei Zhou, Songlin Hu
Fake news’s quick propagation on social media brings severe social ramifications and economic damage.
no code implementations • COLING 2022 • Lingwei Wei, Dou Hu, Wei Zhou, Songlin Hu
In this paper, we propose a novel dual graph-based model, Uncertainty-aware Propagation Structure Reconstruction (UPSR) for improving fake news detection.
1 code implementation • 21 Dec 2023 • Dou Hu, Lingwei Wei, Yaxin Liu, Wei Zhou, Songlin Hu
It can enhance the generalization ability of pre-trained language models for better language understanding.
no code implementations • 29 Nov 2023 • Han Cao, Lingwei Wei, Mengyang Chen, Wei Zhou, Songlin Hu
However, they encounter challenges in effectively handling Chinese fact verification and the entirety of the fact-checking pipeline due to language inconsistencies and hallucinations.
no code implementations • 21 Nov 2023 • Mengyang Chen, Lingwei Wei, Han Cao, Wei Zhou, Songlin Hu
In this paper, we present a comprehensive empirical study to explore the performance of LLMs on misinformation detection tasks.
1 code implementation • 2 Jun 2023 • Dou Hu, Yinan Bao, Lingwei Wei, Wei Zhou, Songlin Hu
To address this, we propose a supervised adversarial contrastive learning (SACL) framework for learning class-spread structured representations in a supervised manner.
Ranked #4 on Emotion Recognition in Conversation on EmoryNLP
1 code implementation • 1 Jun 2023 • Dou Hu, Lingwei Wei, Yaxin Liu, Wei Zhou, Songlin Hu
To alleviate these, we propose a generalized multilingual system SACL-XLMR for sentiment analysis on low-resource languages.
Ranked #1 on Zero-shot Sentiment Classification on AfriSenti
no code implementations • 7 Jun 2022 • Yinan Bao, Qianwen Ma, Lingwei Wei, Wei Zhou, Songlin Hu
Since the dependencies between speakers are complex and dynamic, which consist of intra- and inter-speaker dependencies, the modeling of speaker-specific information is a vital role in ERC.
no code implementations • 4 May 2022 • Yinan Bao, Qianwen Ma, Lingwei Wei, Wei Zhou, Songlin Hu
Besides, a clause graph is also established to model coarse-grained semantic relations between clauses.
1 code implementation • 4 Mar 2022 • Dou Hu, Xiaolong Hou, Lingwei Wei, Lianxin Jiang, Yang Mo
For multimodal ERC, it is vital to understand context and fuse modality information in conversations.
Ranked #23 on Emotion Recognition in Conversation on IEMOCAP
1 code implementation • ACL 2021 • Lingwei Wei, Dou Hu, Wei Zhou, Zhaojuan Yue, Songlin Hu
Detecting rumors on social media is a very critical task with significant implications to the economy, public health, etc.
1 code implementation • 17 Jun 2021 • Dou Hu, Lingwei Wei, Wei Zhou, Xiaoyong Huai, Zhiqi Fang, Songlin Hu
The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting.
Ranked #1 on Session-Based Recommendations on Last.FM
2 code implementations • ACL 2021 • Dou Hu, Lingwei Wei, Xiaoyong Huai
Emotion Recognition in Conversations (ERC) has gained increasing attention for developing empathetic machines.
Ranked #14 on Emotion Recognition in Conversation on EmoryNLP
1 code implementation • 16 Jul 2020 • Dou Hu, Lingwei Wei
Although character-based models using lexicon have achieved promising results for Chinese named entity recognition (NER) task, some lexical words would introduce erroneous information due to wrongly matched words.
Ranked #7 on Chinese Named Entity Recognition on Resume NER
Chinese Named Entity Recognition named-entity-recognition +3
1 code implementation • 16 Jul 2020 • Lingwei Wei, Dou Hu, Wei Zhou, Xuehai Tang, Xiaodan Zhang, Xin Wang, Jizhong Han, Songlin Hu
Furthermore, we design a Sentiment-based Rethinking mechanism (SR) by refining the HIN with sentiment label information to learn a more sentiment-aware document representation.