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Research map / Architectures and efficiency / RNNs and normalization

Argument Mining for Improving the Automated Scoring of Persuasive Essays

AAAI 2018 · Architectures and efficiency › RNNs and normalization · approach: Learned embeddings for retrieval · 84 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add argumentative features to base AES featuresBASE · TOEFL11 cross-foldquadratic-weighted kappa: 0.599 -> 0.611Helped
add argumentative features to base AES featuresBASE · ASAP prompt 2 cross-foldquadratic-weighted kappa: 0.680 -> 0.689Helped
add argumentative features to base AES featuresBASE · ASAP prompt 1 cross-foldquadratic-weighted kappa: 0.831 -> 0.830No clear effect
add argumentative features to base AES featuresBASE · ASAP cross-prompt P1 to P2quadratic-weighted kappa: 0.585 -> 0.622Helped
add argumentative features to base AES featuresBASE · TOEFL11 cross-promptquadratic-weighted kappa: 0.591 -> 0.600Helped
add argumentative features to base AES featuresBASE · TOEFL11 test setquadratic-weighted kappa: 0.604 -> 0.607No clear effect
use only argumentative features without base features (ablation)ARG · TOEFL11 cross-foldquadratic-weighted kappa: 0.599 -> 0.494Hurt
add argument flow (AF) features (ablation)All · TOEFL11 cross-foldquadratic-weighted kappa: 0.604 -> 0.611Helped
add argument flow (AF) features (ablation)All · TOEFL11 cross-promptquadratic-weighted kappa: 0.601 -> 0.600Hurt
add structure typology (TS) features (ablation)All · TOEFL11 and ASAP cross-foldquadratic-weighted kappa: 0.611 -> 0.611 (TOEFL11)No clear effect
add argument component (AC) features (ablation)All · ASAP prompt 2 cross-foldquadratic-weighted kappa: 0.676 -> 0.689Helped
add relation (RL) features (ablation)All · ASAP cross-prompt P1 to P2quadratic-weighted kappa: 0.626 -> 0.622Hurt

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