Papers by Xiaoyi Bao
Exploring Knowledge Filtering for Retrieval-Augmented Discriminative Tasks (2025.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on generative tasks, while its potential in discriminative tasks remains largely unexplored. |
| Approach: | They propose a framework that incorporates knowledge filtering and prediction fusion mechanisms to improve model performance. |
| Outcome: | The proposed framework improves model performance on discriminative tasks by filtering out harmful knowledge and integrating it into the input context. |
Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis (2025.findings-naacl)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Exploring Graph Pre-training for Aspect-based Sentiment Analysis (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies tend to extract the sentiment elements in a generative manner to avoid complex modeling of sentiment elements. |
| Approach: | They propose a generative model with an Element-level Graph Pre-training paradigm and a Task Decomposition Pre- training paradigm to make it generalizable and robust against irregular sentiment quadruples. |
| Outcome: | The proposed model is generalizable and robust against irregular sentiment quadruples. |
Revisiting Classical Chinese Event Extraction with Ancient Literature Information (2025.acl-long)
Copied to clipboard
| 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. |
Opinion Tree Parsing for Aspect-based Sentiment Analysis (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing generative models for aspect-based sentiment analysis lack structure well-formedness guarantees and built-in elements alignments. |
| Approach: | They propose an opinion tree parsing model which parses all sentiment elements from an opinion-tree. |
| Outcome: | The proposed model is much faster than previous models and can explore correlations among sentiment elements. |
Employing Glyphic Information for Chinese Event Extraction with Vision-Language Model (2024.findings-emnlp)
Copied to clipboard
| 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. |
CalligraphicOCR for Chinese Calligraphy Recognition (2025.emnlp-main)
Copied to clipboard
| 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. |
Learning to Look at the Other Side: A Semantic Probing Study of Word Embeddings in LLMs with Enabled Bidirectional Attention (2025.acl-long)
Copied to clipboard
| Challenge: | Autoregressive Large Language Models (LLMs) demonstrate exceptional performance in language understanding and generation tasks, but their application in text embedding tasks has been relatively slow due to the constraints of the unidirectional attention mechanism. |
| Approach: | They propose to enable bidirectional attention in LLMs to improve their ability to represent subsequent context but weaken their utilization of preceding context. |
| Outcome: | The proposed model improves the LLMs’ ability to represent subsequent context but weakens their utilization of preceding context, while contrastive learning training can help to maintain both abilities. |