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This paper utilized a pre-trained DeBERTa-V3 backbone to capture semantic meaning through disentangled attention to capture semantic meaning through disentangled attention, and proposed a Hybrid MSE-CCC Loss to identify distributional relationships that simple regression missed.
Error analysis shows that a non-trivial fraction of failures are placeholder strings caused by API errors rather than incorrect generations, and that surface-level mismatches (verbosity, ortho-graphic variation) account for many of the remaining errors.
A systematic ablation comparing five approaches to identify the more narratively similar of two candidate stories relative to an anchor identifies five systematic failure categories, distinct from the All Correct / Partial cases, including 23 Lexical Trap cases, 23 Hard Cases, and 24 Proposed-Recovery cases.
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