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Research map / Label-efficient and robust learning / Domain adaptation and generalization

Recurrent Neural Networks with Auxiliary Labels for Cross-Domain Opinion Target Extraction

AAAI 2017 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Statistical alignment and hypothesis testing · 117 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Hier-Joint with rule-based auxiliary labels instead of direct source trainingDirect-2 LSTM · Cross-domain OTE average (12 pairs)F1: 0.281 -> 0.342Helped
joint instead of separate training (ablation)Hierarchical network · Cross-domain OTE average (12 pairs)F1: 0.297 -> 0.342Helped
joint instead of separate training (ablation)Concatenated network · Cross-domain OTE average (12 pairs)F1: 0.265 -> 0.280Helped
hierarchical instead of concatenated network (ablation)joint training model · Cross-domain OTE average (12 pairs)F1: 0.280 -> 0.342Helped
naively mixing auxiliary-labeled target data with source dataDirect LSTM · Cross-domain OTE average (12 pairs)F1: 0.265 -> 0.252Hurt
LSTM instead of RNN/BiRNN/BiLSTM (ablation)Hier-Joint · Device targetF1Helped
hidden dim beyond 100 (ablation)Hier-Joint · Device targetF1Hurt
prune rules by source-domain precision (ablation)Hier-Joint · Device targetF1Mixed

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