DARE: Distill and Reinforce Ensemble Neural Networks for Climate-Domain Processing

Abstract

Natural-language processing is well positioned to help stakeholders study the dynamics of ambiguous Climate Change-related (CC) information. Recently, deep neural networks have achieved good results on a variety of NLP tasks depending on high-quality training data and complex and exquisite frameworks. This raises two dilemmas: (1) the networks are highly reliant on powerful hardware devices and processing is time-consuming, which is not only inconducive to execution on edge devices but also leads to resource consumption. (2) Obtaining large-scale effective annotated data is difficult and laborious, especially when it comes to a special domain such as CC. In this paper, we propose a CC-domain-adapted BERT distillation and reinforcement ensemble (DARE) model for tackling the problems above. Specifically, we propose a novel data-augmentation strategy which is a Generator-Reinforced Selector collaboration network for countering the dilemma of CC-related data scarcity. Extensive experimental results demonstrate that our proposed method outperforms baselines with a maximum of 26.83% on SoTA and 50.65× inference time speed-up. Furthermore, as a remedy for the lack of CC-related analysis in the NLP community, we also provide some interpretable conclusions for this global concern.