@inproceedings{song-etal-2022-improving-semantic,
title = "Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion",
author = "Song, Jian and
Liang, Di and
Li, Rumei and
Li, Yuntao and
Wang, Sirui and
Peng, Minlong and
Wu, Wei and
Yu, Yongxin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.4",
doi = "10.18653/v1/2022.findings-emnlp.4",
pages = "45--57",
abstract = "Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in multiple NLP tasks. However, how to efficiently integrate dependency prior structure into pre-trained models to better model complex semantic matching relations is still unsettled. In this paper, we propose the Dependency-Enhanced Adaptive Fusion Attention (DAFA), which explicitly introduces dependency structure into pre-trained models and adaptively fuses it with semantic information. Specifically, (i) DAFA first proposes a structure-sensitive paradigm to construct a dependency matrix for calibrating attention weights. (ii) It adopts an adaptive fusion module to integrate the obtained dependency information and the original semantic signals. Moreover, DAFA reconstructs the attention calculation flow and provides better interpretability. By applying it on BERT, our method achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task.",
}
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<abstract>Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in multiple NLP tasks. However, how to efficiently integrate dependency prior structure into pre-trained models to better model complex semantic matching relations is still unsettled. In this paper, we propose the Dependency-Enhanced Adaptive Fusion Attention (DAFA), which explicitly introduces dependency structure into pre-trained models and adaptively fuses it with semantic information. Specifically, (i) DAFA first proposes a structure-sensitive paradigm to construct a dependency matrix for calibrating attention weights. (ii) It adopts an adaptive fusion module to integrate the obtained dependency information and the original semantic signals. Moreover, DAFA reconstructs the attention calculation flow and provides better interpretability. By applying it on BERT, our method achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task.</abstract>
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%0 Conference Proceedings
%T Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
%A Song, Jian
%A Liang, Di
%A Li, Rumei
%A Li, Yuntao
%A Wang, Sirui
%A Peng, Minlong
%A Wu, Wei
%A Yu, Yongxin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F song-etal-2022-improving-semantic
%X Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching. Meanwhile, dependency prior knowledge has also shown general benefits in multiple NLP tasks. However, how to efficiently integrate dependency prior structure into pre-trained models to better model complex semantic matching relations is still unsettled. In this paper, we propose the Dependency-Enhanced Adaptive Fusion Attention (DAFA), which explicitly introduces dependency structure into pre-trained models and adaptively fuses it with semantic information. Specifically, (i) DAFA first proposes a structure-sensitive paradigm to construct a dependency matrix for calibrating attention weights. (ii) It adopts an adaptive fusion module to integrate the obtained dependency information and the original semantic signals. Moreover, DAFA reconstructs the attention calculation flow and provides better interpretability. By applying it on BERT, our method achieves state-of-the-art or competitive performance on 10 public datasets, demonstrating the benefits of adaptively fusing dependency structure in semantic matching task.
%R 10.18653/v1/2022.findings-emnlp.4
%U https://aclanthology.org/2022.findings-emnlp.4
%U https://doi.org/10.18653/v1/2022.findings-emnlp.4
%P 45-57
Markdown (Informal)
[Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion](https://aclanthology.org/2022.findings-emnlp.4) (Song et al., Findings 2022)
ACL
- Jian Song, Di Liang, Rumei Li, Yuntao Li, Sirui Wang, Minlong Peng, Wei Wu, and Yongxin Yu. 2022. Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 45–57, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.