Papers with policy gradient method

3 papers
Policy Gradient as a Proxy for Dynamic Oracles in Constituency Parsing (P18-2)

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Challenge: Existing models for constituency parsing are transition based, decomposing production of each parse tree into a sequence of action decisions.
Approach: They propose to use a policy gradient method to reduce exposure bias by allowing exploration during training.
Outcome: The proposed method outperforms static oracle likelihood training on four constituency parsers in three languages on four parser-agnostic languages.
Adversarial Grammatical Error Correction (2020.findings-emnlp)

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Challenge: Experimental results show that adversarial-GEC can achieve competitive GEC quality compared to NMT-based baselines.
Approach: They propose an adversarial approach to Grammatical Error Correction using a transformer-based model and a sentence-pair classification model.
Outcome: The proposed approach achieves competitive GEC quality compared to baselines.
Tutoring Helps Students Learn Better: Improving Knowledge Distillation for BERT with Tutor Network (2022.emnlp-main)

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Challenge: Existing knowledge distillation approaches for language models have overlooked the difficulty of training examples.
Approach: They propose a framework that controls difficulty of training examples during pre-training by a tutor network.
Outcome: The proposed framework outperforms state-of-the-art KD methods with student models on the GLUE benchmark.

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