Implicit discourse relation recognition (abbr., IDRR) is a task of automatically classifying semantic relations between pairwise arguments in the absence of explicit connectives. IDRR suffers from imbalanced data distribution of different relations. The current data augmentation approaches have relieved this problem to some extent. However, the incorporated discourse samples during augmentation lack customized contrastive effects. To address this issue, we propose to construct topic-relevant relation-inconsistent arguments for augmentation. Specifically, we implement a Relation-Variant Discourse Recasting (RVDR) approach. RVDR utilizes Qwen3-4B to paraphrase discourses under the forcible constraints of preserving inherent topics and transforming relation senses toward infrequent target relations. To avoid excessive divergent thinking of RVDR, we optimize the Qwen3-4B via reinforcement learning, where Group Relative Policy Optimization (GRPO) is used along with multiple task-specific rewards. Furthermore, rule-based filtering and confidence estimation are used to ensure data quality, which additionally enable two application modes, i.e., Confidence-based and Progressive Data Augmentation (i.e., CDA and PDA). Experiments on PDTB 2.0 and 3.0 show that RVDR-based CDA consistently improves IDRR performance across multiple backbones, including both encoder-only and decoder-only cases. It also allows the strong IDRR frameworks CP-KD and SCDN to be enhanced further. The code is publicly available.

With the development of NLP technologies, news can be automatically categorized and labeled according to a variety of characteristics, at the same time be represented as low dimensional embeddings. However, it lacks a systematic approach that effectively integrates the inherited features and inter-textual knowledge of news to represent the collective information with a dense vector. With the aim of filling this gap, the News2vec model is proposed to allow the distributed representation of news taking into account its associated features. To describe the cross-document linkages between news, a network consisting of news and its attributes is constructed. Moreover, the News2vec model treats the news node as a bag of features by developing the Subnode model. Based on the biased random walk and the skip-gram model, each news feature is mapped to a vector, and the news is thus represented as the sum of its features. This approach offers an easy solution to create embeddings for unseen news nodes based on its attributes. To evaluate our model, dimension reduction plots and correlation heat-maps are created to visualize the news vectors, together with the application of two downstream tasks, the stock movement prediction and news recommendation. By comparing with other established text/sentence embedding models, we show that News2vec achieves state-of-the-art performance on these news-related tasks.