Yang Wu


2022

pdf bib
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors
Yang Wu | Yanyan Zhao | Hao Yang | Song Chen | Bing Qin | Xiaohuan Cao | Wenting Zhao
Findings of the Association for Computational Linguistics: ACL 2022

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed. However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world. We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity. Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment analysis models directly. To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues. Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings. The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels. We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets. Furthermore, our approach can be adapted for other multimodal feature fusion models easily.

pdf bib
Mitigating the Inconsistency Between Word Saliency and Model Confidence with Pathological Contrastive Training
Pengwei Zhan | Yang Wu | Shaolei Zhou | Yunjian Zhang | Liming Wang
Findings of the Association for Computational Linguistics: ACL 2022

Neural networks are widely used in various NLP tasks for their remarkable performance. However, the complexity makes them difficult to interpret, i.e., they are not guaranteed right for the right reason. Besides the complexity, we reveal that the model pathology - the inconsistency between word saliency and model confidence, further hurts the interpretability. We show that the pathological inconsistency is caused by the representation collapse issue, which means that the representation of the sentences with tokens in different saliency reduced is somehow collapsed, and thus the important words cannot be distinguished from unimportant words in terms of model confidence changing. In this paper, to mitigate the pathology and obtain more interpretable models, we propose Pathological Contrastive Training (PCT) framework, which adopts contrastive learning and saliency-based samples augmentation to calibrate the sentences representation. Combined with qualitative analysis, we also conduct extensive quantitative experiments and measure the interpretability with eight reasonable metrics. Experiments show that our method can mitigate the model pathology and generate more interpretable models while keeping the model performance. Ablation study also shows the effectiveness.

pdf bib
PARSE: An Efficient Search Method for Black-box Adversarial Text Attacks
Pengwei Zhan | Chao Zheng | Jing Yang | Yuxiang Wang | Liming Wang | Yang Wu | Yunjian Zhang
Proceedings of the 29th International Conference on Computational Linguistics

Neural networks are vulnerable to adversarial examples. The adversary can successfully attack a model even without knowing model architecture and parameters, i.e., under a black-box scenario. Previous works on word-level attacks widely use word importance ranking (WIR) methods and complex search methods, including greedy search and heuristic algorithms, to find optimal substitutions. However, these methods fail to balance the attack success rate and the cost of attacks, such as the number of queries to the model and the time consumption. In this paper, We propose PAthological woRd Saliency sEarch (PARSE) that performs the search under dynamic search space following the subarea importance. Experiments show that PARSE can achieve comparable attack success rates to complex search methods while saving numerous queries and time, e.g., saving at most 74% of queries and 90% of time compared with greedy search when attacking the examples from Yelp dataset. The adversarial examples crafted by PARSE are also of high quality, highly transferable, and can effectively improve model robustness in adversarial training.

2021

pdf bib
Multimodal Fusion with Co-Attention Networks for Fake News Detection
Yang Wu | Pengwei Zhan | Yunjian Zhang | Liming Wang | Zhen Xu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis
Yang Wu | Zijie Lin | Yanyan Zhao | Bing Qin | Li-Nan Zhu
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

2020

pdf bib
An Iterative Emotion Interaction Network for Emotion Recognition in Conversations
Xin Lu | Yanyan Zhao | Yang Wu | Yijian Tian | Huipeng Chen | Bing Qin
Proceedings of the 28th International Conference on Computational Linguistics

Emotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually implicitly model the emotion interaction between utterances by modeling dialogue context, but the misleading emotion information from context often interferes with the emotion interaction. We noticed that the gold emotion labels of the context utterances can provide explicit and accurate emotion interaction, but it is impossible to input gold labels at inference time. To address this problem, we propose an iterative emotion interaction network, which uses iteratively predicted emotion labels instead of gold emotion labels to explicitly model the emotion interaction. This approach solves the above problem, and can effectively retain the performance advantages of explicit modeling. We conduct experiments on two datasets, and our approach achieves state-of-the-art performance.