The current framework for sequence labeling encompasses a feature extractor and a sequence tagger. This study introduces a unified framework named SLGAN, which harnesses the capabilities of Generative Adversarial Networks to address the challenges associated with Sequence Labeling tasks. SLGAN not only mitigates the limitation of GANs in backpropagating loss to discrete data but also exhibits strong adaptability to various sequence labeling tasks. Unlike traditional GANs, the discriminator within SLGAN does not discriminate whether data originates from the discriminator or the generator; instead, it focuses on predicting the correctness of each tag within the tag sequence. We conducted evaluations on six different tasks spanning four languages, including Chinese, Japanese, and Korean Word Segmentation, Chinese and English Named Entity Recognition, and Chinese Part-of-Speech Tagging. Our experimental results illustrate that SLGAN represents a versatile and highly effective solution, consistently achieving state-of-the-art or competitive performance results, irrespective of the specific task or language under consideration.

Recently, fine-tuning the large pre-trained language models on the labeled sentiment dataset achieves appealing performance. However, the obtained model may not generalize well to the other domains due to the domain shift, and it is expensive to update the entire parameters within the large models. Although some existing domain matching methods are proposed to alleviate the above issues, there are multiple relevant source domains in practice which makes the whole training more costly and complicated. To this end, we focus on the efficient unsupervised multi-source sentiment adaptation task which is more challenging and beneficial for real-world applications. Specifically, we propose to extract multi-layer features from the large pre-trained model, and design a dynamic parameters fusion module to exploit these features for both efficient and adaptive tuning. Furthermore, we propose a novel feature structure matching constraint, which enforces similar feature-wise correlations across different domains. Compared with the traditional domain matching methods which tend to pull all feature instances close, we show that the proposed feature structure matching is more robust and generalizable in the multi-source scenario. Extensive experiments on several multi-source sentiment analysis benchmarks demonstrate the effectiveness and superiority of our proposed framework.