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SSR: Utilizing Simplified Stance Reasoning Process for Robust Stance Detection
Motivated by how humans tackle stance detection tasks, we propose to incorporate the stance reasoning process as task knowledge to assist in learning genuine features and reducing reliance on bias features.
Debiasing Stance Detection Models with Counterfactual Reasoning and Adversarial Bias Learning
Stance detection models may tend to rely on dataset bias in the text part as a shortcut and thus fail to sufficiently learn the interaction between the targets and texts.
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition
Our key intuition is that the sentiment representation of a document is composed of the sentiment representations of all the aspects of that document.
Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification
To this end, we propose the BertMasker network which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts, and uses those masked words to form domain-aware sentence representations.
General Classification
Multi-Domain Sentiment Classification
+3
