Papers by Jinghang Gu
Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis (2025.findings-naacl)
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| Challenge: | Existing methods for inference require multiple sampling with preset size . however, it is a high-cost method that requires multiple sampling . |
| Approach: | They propose a method that combines multiple and single sampling to greatly reduce the cost of multiple sampling without sacrificing performance. |
| Outcome: | The proposed method greatly reduces the cost of multiple sampling without sacrificing performance. |
Sentimental Image Generation for Aspect-based Sentiment Analysis (2025.findings-acl)
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| Challenge: | Recent work on textual Aspect-Based Sentiment Analysis (ABSA) has demonstrated promising performance, but limited semantics derived from raw data. |
| Approach: | They propose a method that provides visual semantics to reinforce textual ABSA by adding additional augmentations to the input data. |
| Outcome: | The proposed method can provide visual semantics to reinforce the textual extraction. |
Revisiting Classical Chinese Event Extraction with Ancient Literature Information (2025.acl-long)
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| Challenge: | Existing studies on classical Chinese event extraction focus on grafting the complex modeling from English or modern Chinese works, neglecting the unique characteristic of this language. |
| Approach: | They propose a Literary Vision-Language Model (VLM) for classical Chinese event extraction . they integrate annotations, historical background and character glyphs to capture the inner- and outer-context information from the sequence. |
| Outcome: | The proposed model can capture the inner- and outer-context information at nearly zero cost. |
Employing Glyphic Information for Chinese Event Extraction with Vision-Language Model (2024.findings-emnlp)
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| Challenge: | Recent studies on event extraction have incorporated a variety of features, including textual elements and annotations. |
| Approach: | They propose a glyphic multi-modal Chinese event extraction model with hieroglyphic images to capture morphological structure from the sequence. |
| Outcome: | The proposed model can extract events from a Chinese and KBP Eval datasets at low cost. |
Affection Driven Neural Networks for Sentiment Analysis (2020.lrec-1)
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| Challenge: | Existing deep neural network models lack mechanisms to highlight important sentiment terms. |
| Approach: | They propose a method to incorporate affective knowledge into deep neural network models by mapping affective influence vectors to an affective impact value and integrating them into long-term memory models to highlight affective terms. |
| Outcome: | The proposed approach improves on three large datasets by 1.0% to 1.5% on the benchmark datasets. |
CalligraphicOCR for Chinese Calligraphy Recognition (2025.emnlp-main)
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| Challenge: | Increasing efforts to digitize calligraphy have rely on isolated character recognition, requiring expensive manual splitting into single characters. |
| Approach: | They propose a calligraphicOCR model with calligraphy image augmentation and action-based corrector targeting the root of the problem. |
| Outcome: | The proposed model outperforms baseline models due to visual variations and domain shifts in semantics and is more accurate than previous models. |
An Effective Incorporating Heterogeneous Knowledge Curriculum Learning for Sequence Labeling (2025.acl-short)
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| Challenge: | Existing approaches to enhance sequence labeling models require data heterogeneity and additional modules. |
| Approach: | They propose a dual-stage curriculum learning framework specifically designed for sequence labeling tasks. |
| Outcome: | The proposed model improves training and accelerates training, mitigating the slow training issue of complex models. |