Papers by Zuohui Fu
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation (P19-1)
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| Challenge: | Rhetoric is a vital element in modern Chinese poetry, and plays an essential role in improving its aesthetics. however, to date, it has not been considered in research on automatic poetry generation. |
| Approach: | They propose a rhetorically controlled encoder-decoder for modern Chinese poetry generation . their model captures various rhetorical patterns in an encoder and incorporates mixtures . |
| Outcome: | The proposed model outperforms state-of-the-art methods in terms of fluency, coherence, meaningfulness, and rhetorical aesthetics. |
Faithfully Explainable Recommendation via Neural Logic Reasoning (2021.naacl-main)
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| Challenge: | Existing models for explainable recommendation have neglected faithfulness of KG reasoning . |
| Approach: | They propose to draw on interpretable logical rules to guide path-reasoning process for explanation generation. |
| Outcome: | The proposed method delivers high-quality recommendations and ascertains the faithfulness of the derived explanation. |
Improving Personalized Explanation Generation through Visualization (2022.acl-long)
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| Challenge: | Existing explainable recommendation models generate repetitive sentences for different items or empty sentences with insufficient details. |
| Approach: | They propose a visual-enhanced approach to generate rating scores and text explanations using visualization generation and text–image matching discrimination. |
| Outcome: | The proposed approach improves both the text quality and the diversity and explainability of the generated explanations. |
Context-Aware Interaction Network for Question Matching (2021.emnlp-main)
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| Challenge: | Existing models focus on word-level local matching and neglect the importance of contextual information. |
| Approach: | They propose a context-aware interaction network to properly align two sequences and infer their semantic relationship by using gate fusion layers. |
| Outcome: | The proposed model can accurately align two sequences and infer their semantic relationship on two question matching datasets. |
Assessing Combinational Generalization of Language Models in Biased Scenarios (2022.aacl-short)
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| Challenge: | Existing work focuses on assessing in-domain knowledge, but shedding light on what pre-trained Language Models learn is important. |
| Approach: | They propose a method to assess a PLM's generalization capacity in biased scenarios by combining component combinations where it could be easy for the PLMs to learn shortcuts from the training corpus. |
| Outcome: | The proposed model can overcome distribution shifts in the training corpus and with sufficient data. |
Data Augmentation with Adversarial Training for Cross-Lingual NLI (2021.acl-long)
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| Challenge: | Existing approaches to train cross-lingual models with labeled data are subpar, resulting in subpar results. |
| Approach: | They propose a data augmentation strategy that enriches data to reflect more diversity in a semantically faithful way and leverages adversarial training regimens to achieve greater robustness. |
| Outcome: | The proposed approach improves cross-lingual inference by leveraging the data to reflect more diversity in a semantically faithful way. |
VIP5: Towards Multimodal Foundation Models for Recommendation (2023.findings-emnlp)
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| Challenge: | Recent advances in foundation models have impeded the ability for these fields to benefit from each other’s advancements. |
| Approach: | They propose to use a multimodal foundation model to unify various modalities and recommendation tasks under the P5 recommendation paradigm to implement personalized prompts. |
| Outcome: | The proposed model will unify visual, textual, and personalization modalities under the P5 recommendation paradigm and will improve recommendation performance and efficiency. |