Papers by Zepeng Zhai
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
Copied to clipboard
| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. |
| Approach: | They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms. |
Beyond the Next Token: Towards Prompt-Robust Zero-Shot Classification via Efficient Multi-Token Prediction (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing methods for zero-shot text classification lack prompt engineering due to prompt brittleness . however, these methods are not effective for zero shot text classifications . |
| Approach: | They propose a method that predicts token probabilities across multiple positions and simulates comprehensive sampling of generation paths in a single run of a language model. |
| Outcome: | The proposed approach improves accuracy and reduces standard deviation by 98% . it maintains comparable performance even without a prompt, reducing the need for prompt engineering . |
USSA: A Unified Table Filling Scheme for Structured Sentiment Analysis (2023.acl-long)
Copied to clipboard
| Challenge: | Structured Sentiment Analysis (SSA) is a problem of bi-lexical dependency parsing . previous studies have cast it as a bottleneck because of overlap and discontinuity issues . |
| Approach: | They propose a bi-lexical dependency parsing graph and a table-filling scheme that addresses overlap and discontinuity issues. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |