Papers by Weitong Ruan
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling (2020.acl-main)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding (2026.findings-acl)
Copied to clipboard
Samuel Osebe, Fan Yang, Junyi Li, Yue Gu, Yongxin Wang, Satyapriya Krishna, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Weitong Ruan
| Challenge: | Large Language Models (LLMs) are evolving rapidly on code generation tasks. |
| Approach: | They propose to automate the vulnerability code benchmark creation with iterative auto validation. |
| Outcome: | The proposed benchmark covers 232 CWE categories across C/C++, Java, and Python languages. |
Contextual Domain Classification with Temporal Representations (2021.naacl-industry)
Copied to clipboard
| Challenge: | Existing studies that incorporate context in SLU have focused on domains where context is limited to a few minutes. |
| Approach: | They propose temporal representations that combine wall-clock second difference and turn order offset information to utilize both recent and distant context in a novel large-scale setup. |
| Outcome: | The proposed model reduces 13.04% of classification errors compared to baseline . previous studies have focused on domains where context is limited to a few minutes . |
SWAN: Semantic Watermarking with Abstract Meaning Representation (2026.acl-long)
Copied to clipboard
Ziping Ye, Gourab Dey, Christos Christodoulopoulos, Charith Peris, Anil Ramakrishna, Weitong Ruan, Aram Galstyan, Kai-Wei Chang, Rahul Gupta, Ninareh Mehrabi
| Challenge: | Existing methods to embed signatures by adjusting token selection preferences during text generation are highly sensitive to paraphrasing and synonyms. |
| Approach: | They propose a framework that embeds watermark signatures into the semantic structure of a sentence using Abstract Meaning Representation (AMR). |
| Outcome: | Empirical evaluation shows SWAN matches state-of-the-art detection performance on unaltered watermarked text while improving robustness against paraphrasing. |
Multi-task Learning of Spoken Language Understanding by Integrating N-Best Hypotheses with Hierarchical Attention (2020.coling-industry)
Copied to clipboard
| Challenge: | Existing methods to integrate hypotheses into speech recognition systems are noisy and can cause information loss. |
| Approach: | They propose to integrate hypotheses into multi-task learning and transfer learning to improve performance. |
| Outcome: | The proposed model improves domain and intent classification by 19% and 37% compared to current methods . the proposed model could recover transcription and rewrite the query for a better understanding . |