Lei Yang


pdf bib
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis
Yakun Yu | Mingjun Zhao | Shi-ang Qi | Feiran Sun | Baoxun Wang | Weidong Guo | Xiaoli Wang | Lei Yang | Di Niu
Findings of the Association for Computational Linguistics: ACL 2023

Multimodal Sentiment Analysis leverages multimodal signals to detect the sentiment of a speaker. Previous approaches concentrate on performing multimodal fusion and representation learning based on general knowledge obtained from pretrained models, which neglects the effect of domain-specific knowledge. In this paper, we propose Contrastive Knowledge Injection (ConKI) for multimodal sentiment analysis, where specific-knowledge representations for each modality can be learned together with general knowledge representations via knowledge injection based on an adapter architecture. In addition, ConKI uses a hierarchical contrastive learning procedure performed between knowledge types within every single modality, across modalities within each sample, and across samples to facilitate the effective learning of the proposed representations, hence improving multimodal sentiment predictions. The experiments on three popular multimodal sentiment analysis benchmarks show that ConKI outperforms all prior methods on a variety of performance metrics.


pdf bib
面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)
Zhengwei Lv (吕政伟) | Lei Yang (杨雷) | Zhizhong Shi (石智中) | Xiao Liang (梁霄) | Tao Lei (雷涛) | Duoxing Liu (刘多星)
Proceedings of the 19th Chinese National Conference on Computational Linguistics



pdf bib
AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums
Zhengwei Lv | Duoxing Liu | Haifeng Sun | Xiao Liang | Tao Lei | Zhizhong Shi | Feng Zhu | Lei Yang
Proceedings of the 13th International Workshop on Semantic Evaluation

Fact checking is an important task for maintaining high quality posts and improving user experience in Community Question Answering forums. Therefore, the SemEval-2019 task 8 is aimed to identify factual question (subtask A) and detect true factual information from corresponding answers (subtask B). In order to address this task, we propose a system based on the BERT model with meta information of questions. For the subtask A, the outputs of fine-tuned BERT classification model are combined with the feature of length of questions to boost the performance. For the subtask B, the predictions of several variants of BERT model encoding the meta information are combined to create an ensemble model. Our system achieved competitive results with an accuracy of 0.82 in the subtask A and 0.83 in the subtask B. The experimental results validate the effectiveness of our system.