Papers by Ruize Xu
IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)
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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang
| Challenge: | Existing systems that use a left-to-right completion paradigm are inefficient and expensive. |
| Approach: | They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation . |
| Outcome: | The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems . |
ComCLIP: Training-Free Compositional Image and Text Matching (2024.naacl-long)
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| Challenge: | erroneous semantics of individual entities are essentially confounders that cause the matching failure. |
| Approach: | They propose a training-free compositional CLIP model which disentangles input images into subjects, objects, and action subimages and composes CLIP’s vision encoder and text encoder to perform evolving matching over compositional text embedding and subimage embeddments. |
| Outcome: | The proposed model mitigates spurious correlations introduced by the pretrained CLIP models and dynamically evaluates the importance of each component. |
Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)
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| Challenge: | Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents. |
| Approach: | They propose a graph-based model that captures factual structures of documents for deepfake detection. |
| Outcome: | The proposed model improves strong base models built with RoBERTa on two public deepfake datasets. |
ConFit v2: Improving Resume-Job Matching using Hypothetical Resume Embedding and Runner-Up Hard-Negative Mining (2025.findings-acl)
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| Challenge: | Existing methods to model resume-job fit are sparse since job seekers apply to only a few jobs. |
| Approach: | They propose two techniques to enhance the encoder’s contrastive training process by augmenting job data with hypothetical reference resume generated by a large language model and creating high-quality hard negatives from unlabeled resume/job pairs using a novel hard-negative mining strategy. |
| Outcome: | The proposed method outperforms ConFit and prior methods on two real-world datasets and achieves an average improvement of 13.8% in recall and 17.5% in nDCG across job-ranking and resume-ranker tasks. |