Papers by Zhiming Mao
Visually Guided Generative Text-Layout Pre-training for Document Intelligence (2024.naacl-long)
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| Challenge: | Prior work shows that pre-training techniques can boost the performance of visual document understanding (VDU) . Xu et al., 2020;; Gu e t al, 2021;; Appalaraju e al. 2022) |
| Approach: | They propose a visually guided generative text-layout pre-training method that optimizes hierarchical language and layout modeling objectives to generate interleaved text and layout sequences. |
| Outcome: | The proposed model can process word-intensive documents of any length and achieves competitive performance over baselines on VDU tasks. |
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation (2023.acl-short)
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| Challenge: | Existing text-based recommendation frameworks that use pretrained language models (PLMs) can improve performance on text-related tasks. |
| Approach: | They propose a unified local- and global-attention Transformer encoder to better model two-level contexts of user history. |
| Outcome: | The proposed framework improves on three text-based recommendation tasks. |
Dynamic Online Conversation Recommendation (2020.acl-main)
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| Challenge: | Existing models that assume static user interests are unable to capture the temporal aspects of user interactions and interest changes over time. |
| Approach: | They propose a neural architecture to exploit changes of user interactions and interests over time to predict which discussions they are likely to enter. |
| Outcome: | The proposed model outperforms state-of-the-art models that assume static user interests and handle future conversations that are unseen during training time. |
DIGAT: Modeling News Recommendation with Dual-Graph Interaction (2022.findings-emnlp)
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| Challenge: | Existing news recommendation methods lack effective news-user feature interaction. |
| Approach: | They propose to use news-graph and user-graph channels to enhance news encodings . they also propose to perform effective feature interaction between news and user graphs based on semantic-augmented graphs. |
| Outcome: | The proposed graph attention networks outperform existing NR methods on the benchmark dataset MIND. |
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding (2021.findings-emnlp)
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| Challenge: | Existing news recommendation models encode news title and content separately without leveraging the structural correlation of user browsing histories to reflect user interests explicitly. |
| Approach: | They propose a news recommendation framework consisting of collaborative news encoding and structural user encode to enhance news and user representation learning. |
| Outcome: | The proposed framework improves the performance of news recommendation on the MIND dataset. |