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GIRNet: Interleaved Multi-Task Recurrent State Sequence Models

AAAI 2019 · Label-efficient and robust learning › Recognition and long-tail · approach: Partial and multi-label learning

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use GIRNet's position-sensitive gated interleaving of auxiliary statesCode-switched sentiment classification · Code-switched sentiment classificationAccuracyHelped
Use GIRNet's position-sensitive gated interleaving of auxiliary statesCode-switched POS tagging · Code-switched POS taggingMacro F1Helped
Use GIRNet with whole-passage sentiment as an auxiliary taskLaptop target-dependent sentiment · LaptopMacro F1Helped
Use GIRNet with whole-passage sentiment as an auxiliary taskRestaurant target-dependent sentiment · RestaurantMacro F1Helped
Use two LSTM layers instead of one in GIRNet (ablation)GIRNet · Code-switched sentiment classificationAccuracyHurt
Use two LSTM layers instead of one in GIRNet (ablation)GIRNet · Code-switched POS taggingAccuracyHurt
Use two LSTM layers instead of one in GIRNet (ablation)GIRNet · LaptopAccuracyHelped
Use two LSTM layers instead of one in GIRNet (ablation)GIRNet · RestaurantAccuracyHurt

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