1.For domain adaptation in the NLP field, powerful pre-trained language models, e.g., BERT, XLNet, can overcome the domain-shift problem to some extent. Thus, the authors should be used as the base encoder for all methods and then compare the efficacy of the transfer parts instead of the simplest n-gram features.
2.The whole procedure is slightly complex. The author formulates the prototypical distribution as a GMM, which has high algorithm complexity. However, formal complexity analysis is absent. The author should provide an analysis of the time complexity and training time of the proposed SAUM method compared with other baselines. Besides, a statistically significant test is absent for performance improvements.
3.The motivation of learning a large margin between different classes is exactly discriminative learning, which is not novel when combined with domain adaptation methods and already proposed in the existing literature, e.g., Unified Deep Supervised Domain Adaptation and Generalization, Saeid et al., ICCV 2017. Contrastive Adaptation Network for Unsupervised Domain Adaptation, Kang et al., CVPR 2019 Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation, Chen et al., AAAI 2019.
However, this paper lacks detailed discussions and comparisons with existing discriminative feature learning methods for domain adaptation.
4.The unlabeled data (2000) from the preprocessed Amazon review dataset (Blitzer version) is perfectly balanced, which is impractical in real-world applications. Since we cannot control the label distribution of unlabeled data during training, the author should also use a more convinced setting as did in Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification, He et al., EMNLP 2018, which directly samples unlabeled data from millions of reviews.
5.The paper lacks some related work about cross-domain sentiment analysis, e.g., End-to-end adversarial memory network for cross-domain sentiment classification, Li et al., IJCAI 2017 Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification, He et al., EMNLP 2018 Hierarchical attention transfer network for cross-domain sentiment classification, Li et al., AAAI 18 Questions:
1.Have the authors conducted the significance tests for the improvements?
2.How fast does this algorithm run or train compared with other baselines?
4.The unlabeled data (2000) from the preprocessed Amazon review dataset (Blitzer version) is perfectly balanced, which is impractical in real-world applications. Since we cannot control the label distribution of unlabeled data during training, the author should also use a more convinced setting as did in Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification, He et al., EMNLP 2018, which directly samples unlabeled data from millions of reviews.