Existing studies on personalized sentiment classification consider a document review as an overall text unit and incorporate backgrounds (i.e., user and product information) to learn sentiment representation. However, it is difficult when these methods meet the current pretrained language models (PLMs) owing to quadratic costs that increase with text length and heterogeneous mixes of randomly initialized background information and textual information initialized from well-pretrained checkpoints during information incorporation. To address these problems, we propose a knowledge-enhanced and parameter-efficient layer normalization (E2LN) for efficient and effective review modeling via leveraging LN in transformer structures. Initially, a knowledge base is introduced that stores well-pretrained checkpoints, structured text information, and background information. Based on such a knowledge base, the ability of LN can be magnified as being a crucial component of transformer structure and then improve the performance of PLMs in downstream tasks. Moreover, the proposed E2LN can make PLMs capable of modeling long document reviews and incorporating background information with parameter-efficient fine-tuning and knowledge injecting. Extensive experimental results were obtained for three document-level sentiment classification benchmark datasets. By comparing the results, the effectiveness and efficiency of the proposed model was demonstrated. Code and Data are released at https://github.com/yoyo-yun/E2LN.

Applying neural models injected with in-domain user and product information to learn review representations of unseen or anonymous users incurs an obvious obstacle in content-based recommender systems. For the generalization of the in-domain classifier, most existing models train an extra plain-text model for the unseen domain. Without incorporating historical user and product information, such a schema makes unseen and anonymous users dissociate from the recommender system. To simultaneously learn the review representation of both existing and unseen users, this study proposed a switch knowledge distillation for domain generalization. A generalization-switch (GSwitch) model was initially applied to inject user and product information by flexibly encoding both domain-invariant and domain-specific features. By turning the status ON or OFF, the model introduced a switch knowledge distillation to learn a robust review representation that performed well for either existing or anonymous unseen users. The empirical experiments were conducted on IMDB, Yelp-2013, and Yelp-2014 by masking out users in test data as unseen and anonymous users. The comparative results indicate that the proposed method enhances the generalization capability of several existing baseline models. For reproducibility, the code for this paper is available at: https://github.com/yoyo-yun/DG_RRR.

Emotion classification on code-mixed texts has been widely used in real-world applications. In this paper, we build a system that participates in the WASSA 2023 Shared Task 2 for emotion classification on code-mixed text messages from Roman Urdu and English. The main goal of the proposed method is to adopt a text-mixed data augmentation for robust code-mixed text representation. We mix texts with both multi-label (track 1) and multi-class (track 2) annotations in a unified multilingual pre-trained model, i.e., XLM-RoBERTa, for both subtasks. Our results show that the proposed text-mixed method performs competitively, ranking first in both tracks, achieving an average Macro F1 score of 0.9782 on the multi-label track and of 0.9329 on the multi-class track.