HAMCap: A Weak-Supervised Hybrid Attention-Based Capsule Neural Network for Fine-Grained Climate Change Debate Analysis

HAMCap: A Weak-Supervised Hybrid Attention-Based Capsule Neural Network for Fine-Grained Climate Change Debate Analysis

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Climate change (CC) has become a central global topic within the multiple branches of social disciplines.Natural Language Processing (NLP) plays a superior role since it has achieved marvelous accomplishments in various application scenarios.However, CC debates are ambiguous and complicated to interpret even for humans, especially when it comes to the aspect-oriented fine-grained level.

Furthermore, the lack of large-scale effective labeled datasets is always a plight encountered in NLP.In this work, we propose a novel weak-supervised Hybrid Attention Masking Capsule Neural Network Cutters (HAMCap) for fine-grained CC debate analysis.Specifically, we use vectors with allocated different weights instead of scalars, and a hybrid attention mechanism is designed in order to better capture and represent information.

By randomly masking with a Partial Context Mask (PCM) mechanism, we can better construct the internal relationship between the aspects and entities and easily obtain a large-scale generated dataset.Considering the uniqueness of linguistics, we propose a Reinforcement Learning-based Generator-Selector mechanism to automatically update and Cyberverse select data that are beneficial to model training.Empirical results indicate that our proposed ensemble model outperforms baselines on downstream tasks with a maximum of 50.

08% on accuracy and 49.48% on F1 scores.Finally, we draw interpretable conclusions about the climate change debate, which is a widespread global concern.