Papers by Qingqing Gao
Positive Text Reframing under Multi-strategy Optimization (2025.coling-main)
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| Challenge: | Existing positive reframing models can be fine-tuned to achieve acceptable results, but generating fluent, diverse text remains a challenge. |
| Approach: | They propose a positive reframing sentiment reward and content preservation reward framework . they propose re-ranking methods that optimize for style and consistency . |
| Outcome: | The proposed framework improves on unconstrained and controlled positive reframing tasks. |
CHROMIC: Chronological Reasoning Across Multi-Panel Comics (2026.eacl-long)
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Bingxuan Hou, Jiayi Lin, Chenyang Zhang, Dapeng Yin, Shuyue Zhu, Qingqing Hong, Mengna Gao, Junli Wang
| Challenge: | Large-scale vision–language models have achieved remarkable progress on various reasoning tasks, but most studies focus on natural photographic images and pay limited attention to multi-panel visual narratives such as comics. |
| Approach: | They propose a benchmark dataset for chronological reasoning in multi-panel comics that covers six types of reasoning questions and spans both Western and Japanese comic styles. |
| Outcome: | The proposed dataset covers six types of reasoning questions and spans both Western and Japanese comic styles. |
CEPT: A Contrast-Enhanced Prompt-Tuning Framework for Emotion Recognition in Conversation (2024.lrec-main)
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| Challenge: | Emotion recognition in conversation research suffers from data imbalance and the presence of similar linguistic expressions for different emotions. |
| Approach: | They propose a Contrast-Enhanced Prompt-Tuning framework that transforms an ERC task into a Masked Language Modeling task and generates the emotion for each utterance in the conversation. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on all three benchmark datasets and excels in recognizing minority emotions. |
CORN: Co-Reasoning Network for Commonsense Question Answering (2022.coling-1)
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| Challenge: | Existing work uses two independent modules to model QA content and external commonsense knowledge graph (KG) Existing research uses two separate modules to create QA contextual text representations and relationships between QA entities. |
| Approach: | They propose a commonsense question answering (QA) model that uses two independent modules to model QA contextual text representation and relationships between QA entities in KG. |
| Outcome: | The proposed model achieves state-of-the-art on QA benchmarks in the CommonsenseQA and OpenBookQA datasets. |