| Challenge: | Existing models for semantic sentence matching lack the ability to capture subtle differences. |
| Approach: | They propose to use a Transformer-based pre-trained language model to capture fine-grained differences in sentence pairs by introducing a dual attention module and a fusion module to learn the aggregation of difference and affinity features. |
| Outcome: | The proposed method is able to capture fine-grained differences in sentence pairs. |
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| Challenge: | Existing work on dependency prior structure integration into pre-trained models is still unclear. |
| Approach: | They propose a dependency-based fusion attention paradigm which explicitly introduces dependency prior structure into pre-trained models and adaptively fuses it with semantic information. |
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DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks (2022.findings-naacl)
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HybridBERT - Making BERT Pretraining More Efficient Through Hybrid Mixture of Attention Mechanisms (2024.naacl-srw)
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Sentence Matching with Syntax- and Semantics-Aware BERT (2020.coling-main)
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| Challenge: | Obtaining sentence representations from BERT-based models is valuable as it takes less time to pre-compute a one-time representation of the data and then use it for the downstream tasks. |
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DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction (2021.emnlp-main)
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tBERT: Topic Models and BERT Joining Forces for Semantic Similarity Detection (2020.acl-main)
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FASTMATCH: Accelerating the Inference of BERT-based Text Matching (2020.coling-main)
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| Challenge: | Recent pre-trained language models have shown state-of-the-art accuracies in text matching. |
| Approach: | They propose a BERT-based text matching model where representations and interactions are decoupled . they propose generating final matching scores using a lightweight attention network . |
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