Data augmentation with Mixup has been proven an effective method to regularize the current deep neural networks. Mixup generates virtual samples and corresponding labels simultaneously by linear interpolation. However, the one-stage generation paradigm and the use of linear interpolation have two defects: (1) The label of the generated sample is simply combined from the labels of the original sample pairs without reasonable judgment, resulting in ambiguous labels. (2) Linear combination significantly restricts the sampling space for generating samples. To address these issues, we propose a novel and effective augmentation method, Global Mixup, based on global clustering relationships. Specifically, we transform the previous one-stage augmentation process into two-stage by decoupling the process of generating virtual samples from the labeling. And for the labels of the generated samples, relabeling is performed based on clustering by calculating the global relationships of the generated samples. Furthermore, we are no longer restricted to linear relationships, which allows us to generate more reliable virtual samples in a larger sampling space. Extensive experiments for CNN, LSTM, and BERT on five tasks show that Global Mixup outperforms previous baselines. Further experiments also demonstrate the advantage of Global Mixup in low-resource scenarios.

Search result diversification aims to balance the relevance and diversity of retrieved documents to satisfy the different information needs of users. Three types of approaches have proliferated: explicit models that are based on explicit features (e.g., subtopic coverage), implicit models that are based on implicit features (e.g., the novelty of documents), and ensemble models that utilize both implicit and explicit features. However, the subtopics used by most explicit and ensemble models are usu-ally mined from queries (e.g., using Google Search Suggestions), which may not match the subtopics covered by the candidate documents. Besides, the implicit features used by most implicit models are formulated as either the similarity of documents or the intent of documents. The former cannot directly reflect the relationships of documents at the subtopic level, while the latter cannot capture non-pairwise relationships among documents. To tackle these issues, we propose a novel model that dynamically mines subtopics from the candidate documents and leverages thehypergraph structure to model the diversity of candidate documents, named HGDIV. Specifically, we dynamically mine subtopics from the candidate documents, rather than mining subtopics from queries or using static subtopics as existing methods do. More importantly, we introduce the hypergraph structure to model the diversity of candidate documents for search result diversification, which can capture the non-pairwise relationships among documents. Furthermore, we innovatively model the global and local subtopic distributions to extract the diversity of candidate documents. Experimental results on the public diversity benchmark TREC datasets demonstrate the superiority of our model over state-of-the-art models.

Pre-trained language models (PLMs) with external knowledge have demonstrated their remarkable performance on a variety of downstream natural language processing tasks.The typical methods of integrating knowledge into PLMs are designing different pre-training tasks and training from scratch, which requires high-end hardware, massive storage resources, and computing time.Prompt learning is an effective approach to tune PLMs for specific tasks, and it can also be used to infuse knowledge.However, most prompt learning methods accept one token as the answer instead of multiple tokens.To tackle this problem, we propose the long-answer prompt learning method (KLAPrompt) to incorporate semantic knowledge in Xinhua Dictionary into pre-trained language models.The proposed method splits the whole answer space into several answer subspaces according to the token's position in the long answer.Extensive experimental results on five datasets demonstrate the effectiveness of our approach.

Session-based Recommendation (SR) aims to predict users' next click based on their behavior within a short period, which is crucial for online platforms. However, most existing SR methods somewhat ignore the fact that user preference is not necessarily strongly related to the order of interactions. Moreover, they ignore the differences in importance between different samples, which limits the model-fitting performance. To tackle these issues, we put forward the method, Mining Interest Trends and Adaptively Assigning Sample Weight, abbreviated as MTAW. Specifically, we model users' instant interest based on their present behavior and all their previous behaviors. Meanwhile, we discriminatively integrate instant interests to capture the changing trend of user interest to make more personalized recommendations. Furthermore, we devise a novel loss function that dynamically weights the samples according to their prediction difficulty in the current epoch. Extensive experimental results on two benchmark datasets demonstrate the effectiveness and superiority of our method.