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A Joint Training Dual-MRC Framework for Aspect Based Sentiment Analysis

AAAI 2021 · Label-efficient and robust learning › Recognition and long-tail · approach: Multilingual language representation pretraining · 215 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use dual-MRC rather than Peng-two-stageABSA triple extraction · 14res, 14lap, 15res, 16resF1: 51.89 -> 70.32; 43.50 -> 55.58; 46.79 -> 57.21; 53.62 -> 67.40Helped
Use dual-MRC rather than Peng-two-stageABSA pair extraction · 14res, 14lap, 15res, 16resF1: 56.10 -> 74.93; 53.85 -> 63.37; 56.23 -> 64.97; 60.04 -> 75.71Helped
Use dual-MRC rather than Peng-two-stageABSA AESC · 14res, 14lap, 15res, 16resF1: 74.19 -> 76.57; 62.34 -> 64.59; 65.79 -> 65.14; 71.73 -> 70.84Mixed
Add right-MRC opinion-term extraction during joint training (ablation)Dual-MRC AESC · 14res, 14lap, 15res, 16resF1: 76.31 -> 76.57; 63.95 -> 64.59; 65.43 -> 65.14; 69.48 -> 70.84Mixed
Add right-MRC sentiment classification during joint training (ablation)Dual-MRC Pair extraction · 14res, 14lap, 15res, 16resF1: 76.33 -> 74.93; 65.26 -> 63.37; 65.21 -> 64.97; 76.61 -> 75.71Hurt
Add right-MRC classification and extraction objectives (ablation)Dual-MRC AE · 14res, 14lap, 15res, 16resF1: 82.80 -> 82.93; 78.35 -> 77.31; 78.22 -> 76.08; 82.16 -> 81.20Mixed

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