Coarse-to-Fine Dual Encoders are Better Frame Identification Learners
Kaikai An Ce Zheng Bofei Gao Haozhe Zhao Baobao Chang
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.