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.

Multimodal sarcasm detection has received considerable attention due to its unique role in social networks. Existing methods often rely on feature concatenation to fuse different modalities or model the inconsistencies among modalities. However, sarcasm is often embodied in local and momentary nuances in a subtle way, which causes difficulty for sarcasm detection. To effectively incorporate these nuances, this paper presents Context-Aware Self-Attention Fusion (CAAF) to integrate local and momentary multimodal information into specific words. Furthermore, due to the instantaneous nature of sarcasm, the connotative meanings of words post-multimodal integration generally deviate from their denotative meanings. Therefore, Word Weight Calculation (WWC) is presented to compute the weight of specific words based on CAAF’s fusion nuances, illustrating the inconsistency between connotation and denotation. We evaluate our method on the MUStARD dataset, achieving an accuracy of 76.9 and an F1 score of 76.1, which surpasses the current state-of-the-art IWAN model by 1.7 and 1.6 respectively.

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.

Chinese Word Segmentation (CWS) intends to divide a raw sentence into words through sequence labeling. Thinking in reverse, CWS can also be viewed as a process of grouping a sequence of characters into a sequence of words. In such a way, CWS is reformed as a separation inference task in every adjacent character pair. Since every character is either connected or not connected to the others, the tagging schema is simplified as two tags “Connection” (C) or “NoConnection” (NC). Therefore, bigram is specially tailored for “C-NC” to model the separation state of every two consecutive characters. Our Separation Inference (SpIn) framework is evaluated on five public datasets, is demonstrated to work for machine learning and deep learning models, and outperforms state-of-the-art performance for CWS in all experiments. Performance boosts on Japanese Word Segmentation (JWS) and Korean Word Segmentation (KWS) further prove the framework is universal and effective for East Asian Languages.