Papers by Chenhe Dong
Enhancing Factual Consistency in Text Summarization via Counterfactual Debiasing (2025.coling-main)
Copied to clipboard
| Challenge: | Abstractive text summarization has produced fluent and informative outputs, but factual inconsistency is a challenge. |
| Approach: | They propose a framework that mitigates the causal effects of language bias and irrelevancy bias by counterfactual estimation. |
| Outcome: | The proposed framework outperforms baseline methods on two widely used summarization datasets. |
Tunable Soft Prompts are Messengers in Federated Learning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to protect model privacy in federated learning (FL) are limited. |
| Approach: | They propose a federated learning approach that provides model privacy protection via tunable soft prompts. |
| Outcome: | The proposed approach provides protection for the global model while reducing communication and computation costs. |
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression (2021.emnlp-main)
Copied to clipboard
| Challenge: | Large pre-trained language models (PLMs) have shown overwhelming performances on many tasks, but their large size and slow inference speed have hindered practical deployments. |
| Approach: | They propose a hierarchical relational knowledge distillation method to capture hierarchic and domain relational information. |
| Outcome: | The proposed method outperforms existing methods on multi-domain datasets and is highly reproducible. |
EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have shown remarkable results on various NLP tasks. |
| Approach: | They propose to improve the feed-forward network (FFN) in BERT with a higher computational cost than improving the multi-head attention (MHA). |
| Outcome: | The proposed model is 6.9 smaller and 4.4 faster than BERTBASE and has competitive performances on GLUE and SQuAD Benchmarks. |