Papers by Dongning Rao
IM^2: an Interpretable and Multi-category Integrated Metric Framework for Automatic Dialogue Evaluation (2022.emnlp-main)
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| Challenge: | Evaluation metrics for dialogue systems are expensive and time-consuming . current evaluation metrics focus on a single quality or several qualities . |
| Approach: | They propose an interpretable, multi-faceted, and controllable framework to combine dialogue metrics which are good at measuring different qualities. |
| Outcome: | The proposed framework integrates a large number of evaluation metrics to improve the performance of the model. |
A Comprehensive Literary Chinese Reading Comprehension Dataset with an Evidence Curation Based Solution (2025.emnlp-main)
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| Challenge: | Low-resource language understanding is challenging for large language models (LLMs). |
| Approach: | They propose a CompRehensive lIterary Chinese readIng comprehenSion procedure with a large dataset for CRISIS. |
| Outcome: | The proposed procedure has the largest dataset and substantiates the effectiveness of the proposed procedure with a 7 percent hike in accuracy compared with the baseline. |
EMPATH: An Ensemble Method for Automatic Fine-Grained Turn-Level Dialogue Empathy Evaluation with a Novel Emotional Distance Metric (2026.findings-acl)
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| Challenge: | Empathy evaluation metrics are lacking in the competitions, and classical dialogue evaluation metrics require further investigation. |
| Approach: | They propose a framework which combines fine-tuned models, large language models, classical dialogue evaluation metrics, and a novel metric. |
| Outcome: | The proposed framework improves on the WASSA 2024 benchmark and shows a statistically significant 8% improvement on the EX dataset. |
STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (2021.emnlp-main)
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| Challenge: | Existing models for text classification are limited in performance, resulting in poor rumor detection. |
| Approach: | They propose to use Chinese microblogs to detect rumors using pre-trained language models and auxiliary features such as comments to mask co-attention. |
| Outcome: | The proposed model outperforms the state-of-the-art on Weibo20 and three existing social media datasets. |
SEHY: A Simple yet Effective Hybrid Model for Summarization of Long Scientific Documents (2022.findings-aacl)
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| Challenge: | Abstractive approaches to extract salient sentences from long documents are not effective due to their size. |
| Approach: | They propose a simple yet effective approach that exploits the discourse information of a document to select salient sections instead of sentences for summary generation. |
| Outcome: | The proposed approach avoids full-text understanding and retains salient information given the length limit. |
LLM-based Open Domain Planning by Leveraging Entity-Attribute-Level Domain Models (2025.findings-emnlp)
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| Challenge: | Currently, large language models (LLMs) based on Open domain Natural language planning have limited application potential. |
| Approach: | They propose a dataset with a baseline for Open domain Natural language planning . the dataset provides the largest dataset for textual procedures to date . |
| Outcome: | The proposed dataset provides the largest dataset for textual procedures to date . it leverages entity-attribute-level action models to reveal relevant physical properties . |
Leveraging Context-Aware Prompting for Commit Message Generation (2024.emnlp-main)
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| Challenge: | Existing methods for writing comprehensive commit messages focus on the changed lines or nearest context lines, but excessive contexts can lead to noise. |
| Approach: | They propose a code model COMMIT that can generate automatic commit messages by combining a dataset with a context-aware prompt. |
| Outcome: | The proposed model surpasses all existing models including pre-trained language models for code and large language models such as Code-LlaMa. |