Where to Perturb? Perturbation Allocation in Representation-Space Augmentation for Sparse Event Detection

Representation-space augmentation can conflate how much an input is perturbed with where perturbation acts. We separate these factors in sparse event detection by calibrating post-normalization squared displacement and independently varying training allocation and test channel. In the frozen-representation CASIE setting under strong Gaussian stress, sentence-only and token-only training reverse their Trigger F1 ordering between the sentence and token test endpoints in 14 of 15 paired seeds. Averaged equally over five test allocations, however, sentence-only training has a $3.53$-point advantage. Joint training improves the token-endpoint mean over sentence-only training while sacrificing performance at the sentence endpoint, illustrating a tradeoff rather than a uniform gain. Aggregate rankings therefore depend on the weights assigned to evaluation channels. Positive allocation–channel interactions persist across frozen-setting controls for distance, loss, encoder, and classifier, and extend to exact-span detection on PHEE. The corresponding interaction estimates are near zero under the tested end-to-end protocol. Local gradients show lower sensitivity to the trained-noisy path and higher sensitivity to the other path; permutation diagnostics provide complementary evidence consistent with a redistribution of path reliance. These results show why augmentation comparisons should report conditional robustness profiles alongside total displacement and specify the optimization and evaluation protocols under which they hold.