Xiang Chen


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Good Visual Guidance Make A Better Extractor: Hierarchical Visual Prefix for Multimodal Entity and Relation Extraction
Xiang Chen | Ningyu Zhang | Lei Li | Yunzhi Yao | Shumin Deng | Chuanqi Tan | Fei Huang | Luo Si | Huajun Chen
Findings of the Association for Computational Linguistics: NAACL 2022

Multimodal named entity recognition and relation extraction (MNER and MRE) is a fundamental and crucial branch in information extraction. However, existing approaches for MNER and MRE usually suffer from error sensitivity when irrelevant object images incorporated in texts.To deal with these issues, we propose a novel Hierarchical Visual Prefix fusion NeTwork (HVPNeT) for visual-enhanced entity and relation extraction, aiming to achieve more effective and robust performance. Specifically, we regard visual representation as pluggable visual prefix to guide the textual representation for error insensitive forecasting decision. We further propose a dynamic gated aggregation strategy to achieve hierarchical multi-scaled visual features as visual prefix for fusion. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our method, and achieve state-of-the-art performance.


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ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning
Xin Xie | Xiangnan Chen | Xiang Chen | Yong Wang | Ningyu Zhang | Shumin Deng | Huajun Chen
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM). We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model. Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model. Evaluation results demonstrate the effectiveness of our proposed approach. Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively. We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches. The code and dataset used in our paper can be found at https://github.com/CheaSim/SemEval2021. The leaderboard can be found at https://competitions.codalab.org/competitions/26153.

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WIND: Weighting Instances Differentially for Model-Agnostic Domain Adaptation
Xiang Chen | Yue Cao | Xiaojun Wan
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Few-Shot Intent Detection via Contrastive Pre-Training and Fine-Tuning
Jianguo Zhang | Trung Bui | Seunghyun Yoon | Xiang Chen | Zhiwei Liu | Congying Xia | Quan Hung Tran | Walter Chang | Philip Yu
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection schema via contrastive pre-training and fine-tuning. Specifically, we first conduct self-supervised contrastive pre-training on collected intent datasets, which implicitly learns to discriminate semantically similar utterances without using any labels. We then perform few-shot intent detection together with supervised contrastive learning, which explicitly pulls utterances from the same intent closer and pushes utterances across different intents farther. Experimental results show that our proposed method achieves state-of-the-art performance on three challenging intent detection datasets under 5-shot and 10-shot settings.