Multi-Domain Sentiment Classification
1 papers with code • 1 benchmarks • 1 datasets
Latest papers with no code
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks
Recent work in black-box adversarial attacks for NLP systems has attracted much attention.
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.
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks
Recent work in black-box adversarial attacks for NLP systems has attracted attention.
Dual Adversarial Co-Learning for Multi-Domain Text Classification
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC).
Learning Domain Representation for Multi-Domain Sentiment Classification
It is useful to leveraging data available for all existing domains to enhance performance on different domains.