Papers by Fengyu Zhou
Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems (2021.emnlp-main)
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| Challenge: | Large-scale pre-trained language models have shown promising results for few-shot learning in task-oriented dialog (ToD) systems. |
| Approach: | They propose a self-training approach that iteratively labels the most confident unlabeled data to train a stronger Student model. |
| Outcome: | The proposed approach improves state-of-the-art pre-trained models in few-shot learning scenarios for task-oriented dialog (ToD) systems when only a small number of labeled data are available. |
InteMATs: Integrating Granularity-Specific Multilingual Adapters for Cross-Lingual Transfer (2023.findings-emnlp)
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| Challenge: | Existing work relies on full-model fine-tuning on large parallel datasets to enhance cross-lingual alignment of MLLMs. |
| Approach: | They propose an approach that integrates multilingual adapters trained on texts of different levels of granularity into multilingual models. |
| Outcome: | The proposed approach improves the performance of multilingual language models on low-resource languages. |
Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistency (2022.findings-naacl)
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| Challenge: | Existing knowledge graph-to-text generation methods focus on sequence-to sequence generation, but the linearized order of KG is obtained through a heuristic search without data-driven optimization. |
| Approach: | They propose to generate easy-to-understand sentences from the knowledge graph . they incorporate part-of-speech syntactic tags to constrain the positions to copy words from the KG and employ a semantic context scoring function to evaluate the semantic fitness for each word in its local context. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets, WebNLG and DART, and achieves high consistency. |