Papers by Weitong Chen
Automatic, Meta and Human Evaluation for Multimodal Summarization with Multimodal Output (2024.naacl-long)
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| Challenge: | Multimodal summarization with multimodal output (MSMO) has attracted increasing research interest . evaluation is an emerging yet underexplored research topic . |
| Approach: | They propose a framework that studies three research questions of MSMO evaluation . they propose an automatic evaluation metric and a meta-evaluation benchmark dataset . |
| Outcome: | The proposed evaluation metric and human-annotated meta-evaluation benchmark are used to assess the quality of evaluation metrics and show the framework is effective. |
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (2020.acl-main)
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| Challenge: | Empirical studies show that virtual adversarial training (VAT) significantly improves the sequence labeling performance over baselines under supervised and semi-supervised settings. |
| Approach: | They propose a method which naturally applies VAT to sequence labeling models with conditional random field (CRF) Empirical studies show that SeqVAT significantly improves the sequence labelling performance over baselines under supervised settings, and outperforms state-of-the-art approaches under semi-supervised settings. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art approaches under semi-supervised settings. |
Enhance Robustness of Sequence Labelling with Masked Adversarial Training (2020.findings-emnlp)
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| Challenge: | Adversarial training (AT) has shown strong regularization effects on deep learning algorithms by introducing small input perturbations to improve model robustness. |
| Approach: | They propose to use adversarial training to improve robustness from contextual information in sequence labelling tasks by masking or replacing some words in the sentence. |
| Outcome: | The proposed method shows significant improvements on accuracy and robustness of sequence labelling on CoNLL 2000 and 2003 benchmarks. |
Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time (2025.emnlp-main)
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| Challenge: | Recent advances in Multimodal Large Language Models have raised serious safety concerns. |
| Approach: | They propose a method for manipulating the output preference of MLLMs using a preference hijacked image. |
| Outcome: | The proposed method works at inference time and requires no model modifications. |