Zhewen Yu


pdf bib
Dual-Encoder Transformers with Cross-modal Alignment for Multimodal Aspect-based Sentiment Analysis
Zhewen Yu | Jin Wang | Liang-Chih Yu | Xuejie Zhang
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Multimodal aspect-based sentiment analysis (MABSA) aims to extract the aspect terms from text and image pairs, and then analyze their corresponding sentiment. Recent studies typically use either a pipeline method or a unified transformer based on a cross-attention mechanism. However, these methods fail to explicitly and effectively incorporate the alignment between text and image. Supervised finetuning of the universal transformers for MABSA still requires a certain number of aligned image-text pairs. This study proposes a dual-encoder transformer with cross-modal alignment (DTCA). Two auxiliary tasks, including text-only extraction and text-patch alignment are introduced to enhance cross-attention performance. To align text and image, we propose an unsupervised approach which minimizes the Wasserstein distance between both modalities, forcing both encoders to produce more appropriate representations for the final extraction. Experimental results on two benchmarks demonstrate that DTCA consistently outperforms existing methods.


pdf bib
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing
Zhewen Yu | Jin Wang | Xuejie Zhang
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

Data sharing restrictions are common in NLP datasets. The purpose of this task is to develop a model trained in a source domain to make predictions for a target domain with related domain data. To address the issue, the organizers provided the models that fine-tuned a large number of source domain data on pre-trained models and the dev data for participants. But the source domain data was not distributed. This paper describes the provided model to the NER (Name entity recognition) task and the ways to develop the model. As a little data provided, pre-trained models are suitable to solve the cross-domain tasks. The models fine-tuned by large number of another domain could be effective in new domain because the task had no change.