The nonstationary nature of real-world multivariate time series (MTS) data presents forecasting models with a formidable challenge of the time-variant distribution of time series, referred to as distribution shift. Existing studies on the distribution shift mostly adhere to adaptive normalization techniques for alleviating temporal mean and covariance shifts or time-variant modeling for capturing temporal shifts. Despite improving model generalization, these normalization-based methods often assume a time-invariant transition between outputs and inputs but disregard specific intraseries/interseries correlations, while time-variant models overlook the intrinsic causes of the distribution shift. This limits the model's expressiveness and interpretability in tackling the distribution shift for MTS forecasting. To mitigate such a dilemma, we present a unified Probabilistic Graphical Model to Jointly capture intraseries/interseries correlations and model the time-variant transitional distribution and instantiate a neural framework called JointPGM for nonstationary MTS forecasting. Specifically, JointPGM first employs multiple Fourier basis functions to learn dynamic time factors and designs two distinct learners: intraseries and interseries learners. The intraseries learner effectively captures temporal dynamics by utilizing temporal gates, while the interseries learner explicitly models spatial dynamics through multihop propagation, incorporating Gumbel-softmax sampling. These two types of series dynamics are subsequently fused into a latent variable, which is inversely employed to infer time factors, generate a final prediction, and perform the reconstruction. We validate the effectiveness and efficiency of JointPGM through extensive experiments on six highly nonstationary MTS datasets, achieving state-of-the-art (SOTA) forecasting performance of MTS forecasting.

Few-shot named entity recognition (NER) aims to recognize novel named entities in low-resource domains utilizing existing knowledge. However, the present few-shot NER models assume that the labeled data are all clean without noise or outliers, and there are few works focusing on the robustness of the cross-domain transfer learning ability to textual adversarial attacks in Few-shot NER. In this work, we comprehensively explore and assess the robustness of few-shot NER models under textual adversarial attack scenario, and found the vulnerability of existing few-shot NER models. Furthermore, we propose a robust two-stage few-shot NER method with Boundary Discrimination and Correlation Purification (BDCP). Specifically, in the span detection stage, the entity boundary discriminative module is introduced to provide a highly distinguishing boundary representation space to detect entity spans. In the entity typing stage, the correlations between entities and contexts are purified by minimizing the interference information and facilitating correlation generalization to alleviate the perturbations caused by textual adversarial attacks. In addition, we construct adversarial examples for few-shot NER based on public datasets Few-NERD and Cross-Dataset. Comprehensive evaluations on those two groups of few-shot NER datasets containing adversarial examples demonstrate the robustness and superiority of the proposed method.

Tuples extraction is a fundamental task for information extraction and knowledge graph construction.The extracted tuples are usually represented as knowledge triples consisting of subject, relation, and object.In practice, however, the validity of knowledge triples is associated with and changes with the spatial, temporal, or other kinds of constraints.Motivated by this observation, this paper proposes a constrained tuple extraction (CTE) task to guarantee the validity of knowledge tuples.Formally, the CTE task is to extract constrained tuples from unstructured text, which adds constraints to conventional triples.To this end, we propose an interaction-aware network.Combinatorial interactions among context-specific external features and distinct-granularity internal features are exploited to effectively mine the potential constraints.Moreover, we have built a new dataset containing totally 1,748,826 constrained tuples for training and 3656 ones for evaluation.Experiments on our dataset and the public CaRB dataset demonstrate the superiority of the proposed model.The constructed dataset and the codes are publicly available.

Multimodal sentiment analysis (MSA) is a very challenging task due to its complex and complementary interactions between multiple modalities, which can be widely applied into areas of product marketing, public opinion monitoring, and so on. However, previous works directly utilized the features extracted from multimodal data, in which the noise reduction within and among multiple modalities has been largely ignored before multimodal fusion. This paper proposes a multi-level attention map network (MAMN) to filter noise before multimodal fusion and capture the consistent and heterogeneous correlations among multi-granularity features for multimodal sentiment analysis. Architecturally, MAMN is comprised of three modules: multi-granularity feature extraction module, multi-level attention map generation module, and attention map fusion module. The first module is designed to sufficiently extract multi-granularity features from multimodal data. The second module is constructed to filter noise and enhance the representation ability for multi-granularity features before multimodal fusion. And the third module is built to extensibly mine the interactions among multi-level attention maps by the proposed extensible co-attention fusion method. Extensive experimental results on three public datasets show the proposed model is significantly superior to the state-of-the-art methods, and demonstrate its effectiveness on two tasks of document-based and aspect-based MSA tasks.