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 $\underline{Co}$arse-to-$\underline{F}$ine $\underline{F}$rame and $\underline{T}$arget $\underline{E}$ncoders $\underline{A}$rchitecture. 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.

CoFFTEA提出一种粗到细的框架和目标编码体系结构，通过对比学习和双编码器，有效地模拟了框架和目标之间的对齐，实验结果表明，CoFFTEA的整体得分比以前的模型高0.93，并且在没有lexicon filtering的情况下R@1提高了1.53，进一步分析显示，CoFFTEA能够更好地建模框架与框架之间以及目标与目标之间的关系。

粗到细双编码器更好的帧识别学习器