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    <abstract>Some dialogue corpus projects use a verify-after-annotation workflow: a second team member reviews a submitted file and records corrections. The resulting correction count mixes two signals, annotator accuracy and verifier strictness. We separate these signals for RASwDA, an audio-anchored re-alignment of 1,045 Switchboard file sides (105,005 corrections across 977 change logs) produced by five team members in 2024-2025. Verification is crossed: each of three identified annotators was checked by four different verifiers, with overlap in both directions. A cross-classified mixed-effects model on per-file corrections-per-interval assigns 10.8% of the variance to annotator identity, while the verifier random effect collapses to zero (singular fit, stable across seven leave-one-out and response-choice refits). Thus, for this boundary-realignment task, we find no detectable verifier identity effect once annotator identity, batch, and file length are controlled. Boundary placement, not label selection, accounts for 58.5% of corrections corpus-wide. This helps explain why verifier-specific strictness has little room to appear: timestamp adjustments are anchored in the audio, while DA-label changes account for only 3.0% of corrections. We release the action-typed change logs so other projects can run the same annotator-verifier decomposition on their own verification data.</abstract>
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%0 Conference Proceedings
%T Disentangling Annotator Skill from Verifier Strictness in Cross-Verified Dialogue Annotation
%A Tao, Zihao
%A Prado, John A.
%A LaManna, Ignazio Steven
%A Puterbaugh, Ryan
%A Datta, Mim
%A Hirschberg, Julia
%Y Choi, Jinho D.
%Y Chen, Yun-Nung
%Y Funakoshi, Kotaro
%Y Emami, Ali
%S Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2026
%8 August
%I Association for Computational Linguistics
%C Atlanta, Georgia, USA
%F tao-etal-2026-disentangling
%X Some dialogue corpus projects use a verify-after-annotation workflow: a second team member reviews a submitted file and records corrections. The resulting correction count mixes two signals, annotator accuracy and verifier strictness. We separate these signals for RASwDA, an audio-anchored re-alignment of 1,045 Switchboard file sides (105,005 corrections across 977 change logs) produced by five team members in 2024-2025. Verification is crossed: each of three identified annotators was checked by four different verifiers, with overlap in both directions. A cross-classified mixed-effects model on per-file corrections-per-interval assigns 10.8% of the variance to annotator identity, while the verifier random effect collapses to zero (singular fit, stable across seven leave-one-out and response-choice refits). Thus, for this boundary-realignment task, we find no detectable verifier identity effect once annotator identity, batch, and file length are controlled. Boundary placement, not label selection, accounts for 58.5% of corrections corpus-wide. This helps explain why verifier-specific strictness has little room to appear: timestamp adjustments are anchored in the audio, while DA-label changes account for only 3.0% of corrections. We release the action-typed change logs so other projects can run the same annotator-verifier decomposition on their own verification data.
%U https://aclanthology.org/2026.sigdial-1.29/
%P 414-422

Markdown (Informal)

[Disentangling Annotator Skill from Verifier Strictness in Cross-Verified Dialogue Annotation](https://aclanthology.org/2026.sigdial-1.29/) (Tao et al., SIGDIAL 2026)

• Disentangling Annotator Skill from Verifier Strictness in Cross-Verified Dialogue Annotation (Tao et al., SIGDIAL 2026)

ACL

• Zihao Tao, John A. Prado, Ignazio Steven LaManna, Ryan Puterbaugh, Mim Datta, and Julia Hirschberg. 2026. Disentangling Annotator Skill from Verifier Strictness in Cross-Verified Dialogue Annotation. In Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 414–422, Atlanta, Georgia, USA. Association for Computational Linguistics.