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    <abstract>Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack sufficient representation learning of the definitions or face challenges in efficiently selecting the most suitable frame from over 1000 candidate frames. Moreover, commonly used lexicon filtering (lf) to obtain candidate frames for the target may ignore out-of-vocabulary targets and cause inadequate frame modeling. In this paper, we propose CoFFTEA, a \underlineCoarse-to-\underlineFine \underlineFrame and \underlineTarget \underlineEncoders \underlineArchitecture. With contrastive learning and dual encoders, CoFFTEA efficiently and effectively models the alignment between frames and targets. By employing a coarse-to-fine curriculum learning procedure, CoFFTEA gradually learns to differentiate frames with varying degrees of similarity. Experimental results demonstrate that CoFFTEA outperforms previous models by 0.93 overall scores and 1.53 R@1 without lf. Further analysis suggests that CoFFTEA can better model the relationships between frame and frame, as well as target and target. The code for our approach is available at https://github.com/pkunlp-icler/COFFTEA.</abstract>
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%0 Conference Proceedings
%T Coarse-to-Fine Dual Encoders are Better Frame Identification Learners
%A An, Kaikai
%A Zheng, Ce
%A Gao, Bofei
%A Zhao, Haozhe
%A Chang, Baobao
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F an-etal-2023-coarse
%X Frame identification aims to find semantic frames associated with target words in a sentence. Recent researches measure the similarity or matching score between targets and candidate frames by modeling frame definitions. However, they either lack sufficient representation learning of the definitions or face challenges in efficiently selecting the most suitable frame from over 1000 candidate frames. Moreover, commonly used lexicon filtering (lf) to obtain candidate frames for the target may ignore out-of-vocabulary targets and cause inadequate frame modeling. In this paper, we propose CoFFTEA, a \underlineCoarse-to-\underlineFine \underlineFrame and \underlineTarget \underlineEncoders \underlineArchitecture. With contrastive learning and dual encoders, CoFFTEA efficiently and effectively models the alignment between frames and targets. By employing a coarse-to-fine curriculum learning procedure, CoFFTEA gradually learns to differentiate frames with varying degrees of similarity. Experimental results demonstrate that CoFFTEA outperforms previous models by 0.93 overall scores and 1.53 R@1 without lf. Further analysis suggests that CoFFTEA can better model the relationships between frame and frame, as well as target and target. The code for our approach is available at https://github.com/pkunlp-icler/COFFTEA.
%R 10.18653/v1/2023.findings-emnlp.897
%U https://aclanthology.org/2023.findings-emnlp.897
%U https://doi.org/10.18653/v1/2023.findings-emnlp.897
%P 13455-13466

Markdown (Informal)

[Coarse-to-Fine Dual Encoders are Better Frame Identification Learners](https://aclanthology.org/2023.findings-emnlp.897) (An et al., Findings 2023)

• Coarse-to-Fine Dual Encoders are Better Frame Identification Learners (An et al., Findings 2023)

ACL

• Kaikai An, Ce Zheng, Bofei Gao, Haozhe Zhao, and Baobao Chang. 2023. Coarse-to-Fine Dual Encoders are Better Frame Identification Learners. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 13455–13466, Singapore. Association for Computational Linguistics.