Zero-shot stance detection aims to determine the stance of previously unseen targets during the inference phase. Achieving effective feature alignment from seen targets to unseen targets is crucial for zero-shot stance detection. In this paper, we propose MSFR, a hierarchical contrastive learning framework, which consists of two core components: inter-aspect contrastive learning for distinguishing aspect-level features and intra-aspect contrastive learning for capturing attribute-level features. Specifically, inter-aspect contrastive learning first maps the global features of an utterance to multiple aspects that influence semantic expression (referred to as aspect-level feature differentiation). This process facilitates the alignment of semantic features across different factors of seen and unseen targets. Intra-aspect contrastive learning enhances the distinguishability of features within the same aspect (referred to as attribute-level feature differentiation) and improves the model’s fine-grained generalization capability. Experimental results demonstrate the superior performance of our model compared to competing baseline models.

In conventional federated learning, each device is restricted to train a network model of a same structure. This greatly hinders the application of federated learning in edge devices and IoT scenarios where the data and devices are quite heterogeneous because of their different hardware equipment and communication networks. At the same time, most of the existing studies about federated learning of heterogeneous models are limited to horizontal heterogeneity which share a highly homogeneous vertical structure. Little work has been done on vertical heterogeneity such as models with different number of functional layers or different connection methods within them, not to mention the integrated heterogeneity scenarios. In DraftFed, a novel draft-based approach is proposed to implement personalized federated learning for integrated heterogeneous models. Unlike traditional federated learning in which the parameters/gradients are exchanged, DraftFed uses drafts as key knowledge to guide mutual learning of models, which makes it suitable for model structure personalization application scenarios..

Zero-shot stance detection is challenging because it requires detecting the stance of previously unseen targets in the inference phase. The ability to learn transferable target-invariant features is critical for zero-shot stance detection. In this paper, we propose a stance detection approach that can efficiently adapt to unseen targets, the core of which is to capture target-invariant syntactic expression patterns as transferable knowledge. Specifically, we first augment the data by masking the topic words of sentences, and then feed the augmented data to an unsupervised contrastive learning module to capture transferable features. Besides, to fit a specific target, we encode the raw text as target-specific features. Finally, we adopt an attention mechanism, which combines syntactic expression patterns with target-specific features to obtain enhanced features for predicting previously unseen targets. Experiments demonstrate that our model outperforms competitive baselines on four benchmark datasets.

Hate speech detection is hampered by the scarcity and topical and lexical biases of annotated data, leading to poor generalization. It is imperative to devise a cross-domain approach to solve this problem. The ability to learn transferable knowledge is critical for cross-domain hate speech detection. In this work, We propose a domain-adaptive dependency graph method based on post-hoc explanation (DPDG). We extract post-hoc explanations from fine-tuned BERT classifiers as the importance score for hate representation. Based on these, we construct in-domain graph and cross-domain graph to better learn in-domain hate representation and adapt to the target domain respectively. Finally, we use interactive GCN blocks to interactively and adaptively learn and adjust the domain adaptive graph representation. The results of cross-domain experiments on multiple domains show that our proposed model outperforms competitive baselines in cross-domain hate speech detection.

State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks.

Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub11https://github.com/zoujiaying1995/sscl-project.

The accurate forecasting of infectious epidemic diseases is the key to effective control of the epidemic situation in a region.Most existing methods ignore potential dynamic dependencies between regions or the importance of temporal dependencies and inter-dependencies between regions for prediction.In this paper, we propose an Interand Intra-Series Embeddings Fusion Network (SEFNet) to improve epidemic prediction performance.SEFNet consists of two parallel modules, named Inter-Series Embedding Module and Intra-Series Embedding Module.In Inter-Series Embedding Module, a multiscale unified convolution component called Region-Aware Convolution is proposed, which cooperates with self-attention to capture dynamic dependencies between time series obtained from multiple regions.The Intra-Series Embedding Module uses Long Short-Term Memory to capture temporal relationships within each time series.Subsequently, we learn the influence degree of two embeddings and fuse them with the parametric-matrix fusion method.To further improve the robustness, SEFNet also integrates a traditional autoregressive component in parallel with nonlinear neural networks.Experiments on four real-world epidemic-related datasets show SEFNet is effective and outperforms state-of-the-art baselines.