Papers with LCQMC
LCQMC:A Large-scale Chinese Question Matching Corpus (C18-1)
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| Challenge: | Existing methods for question answering system lack large-scale question matching corpora . lack of large-sized question matching results in problem solving . |
| Approach: | They propose a large-scale Chinese question matching corpus which is released to the public . they use a search engine to collect large-sized question pairs related to high-frequency words . |
| Outcome: | The proposed corpus is more general than paraphrase corpus as it focuses on intent matching rather than paraphrasing. |
DASA-Trans-STM: Adaptive Efficient Transformer for Short Text Matching using Data Augmentation and Semantic Awareness (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shown impressive versatility across various tasks. |
| Approach: | They propose a novel adaptive Transformer for Chinese short text matching using data augmentation and semantic awareness. |
| Outcome: | The proposed model can deal with word ambiguity in Chinese on four available datasets. |
GBT: Generative Boosting Training Approach for Paraphrase Identification (2023.findings-emnlp)
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| Challenge: | Paraphrase Identification (PI) is a fundamental natural language understanding task with non-trivial challenges. |
| Approach: | They propose a Generative Boosting Training approach for Paraphrase Identification (PI) they use a seq2seq model to perform DA on misclassified instances periodically . |
| Outcome: | The proposed method outperforms state-of-the-art PI models on English and Chinese PI tasks with good efficiency and effectiveness. |
Multi-Granularity Fusion Text Semantic Matching Based on WoBERT (2024.lrec-main)
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| Challenge: | Existing text-matching methods struggle with semantic nuances in short texts . a novel approach to improve text semantic matching is being developed . |
| Approach: | They propose a multi-granularity fusion model that harnesses a pre-trained language model to capture text semantic nuances. |
| Outcome: | The proposed model improves on Chinese short text matching datasets compared to traditional methods . the proposed model captures individual text semantic nuances and improves accuracy . |