Papers by Guoqing Zhao
Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)
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| Challenge: | Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient. |
| Approach: | They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve. |
| Outcome: | The proposed approach improves on three SLT datasets conducted under the gloss-free setting. |
PM2F2N: Patient Multi-view Multi-modal Feature Fusion Networks for Clinical Outcome Prediction (2022.findings-emnlp)
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| Challenge: | Existing methods focused on time series data but ignored clinical notes . fusion of multi-modal features of patients from different views is not feasible due to the time series and clinical notes data being stored as time series. |
| Approach: | They propose to combine time series and clinical notes to fuse multi-modal features of patients from different perspectives using graph neural networks. |
| Outcome: | The proposed method is superior to existing models on MIMIC-III benchmark. |
MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion (2022.emnlp-main)
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| Challenge: | Existing methods to predict missing entities share relation representation across modalities, which results in mutual interference between modality. |
| Approach: | They propose a framework for multimodal knowledge graph completion that learns modality-split relation embeddings for each modality instead of a single modality shared one. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three KG datasets. |
Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances (2022.coling-1)
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| Challenge: | Existing VQA models rely on the superficial correlation between question type and frequent answers to make predictions, without really understanding the input. |
| Approach: | They propose a training framework that explicitly encourages the VQA model to distinguish between superficially similar instances. |
| Outcome: | The proposed framework achieves state-of-the-art performance on VQA-CP v2 . it explicitly encourages the model to distinguish between the superficially similar instances . |
Improving Zero-Shot Entity Linking Candidate Generation with Ultra-Fine Entity Type Information (2022.coling-1)
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| Challenge: | Entity linking is a task of assigning entity mentions to referent entities in a knowledge base. |
| Approach: | They propose to use ultra-fine-grained type information to improve the generalization ability of EL models by utilizing a low-level task to extract ultra-finish entity type information. |
| Outcome: | The proposed model achieves state-of-the-art in the zero-shot entity linking task . |
TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding (2022.coling-1)
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| Challenge: | Existing methods to automatically assign ICD codes ignore crucial information contained in structured medical data, which is hard to be captured from the noisy clinical notes. |
| Approach: | They propose to use a Tree-enhanced multimodal attention network to fuse tabular features and textual features into multimodal representations by enhancing the text representations with tree-based features. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two MIMIC datasets. |
A Span-based Multimodal Variational Autoencoder for Semi-supervised Multimodal Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for named entity recognition on social media are not efficient for semi-supervised MNER because of the mismatch between the posted text and image. |
| Approach: | They propose a novel method to fuse the text and image features for multimodal named entity recognition under semi-supervised setting by exploiting modal-specific VAEs. |
| Outcome: | The proposed method outperforms baselines under supervised setting and improves performance with less labeled data than existing semi-supervised methods. |