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.

Implicit Discourse Relation Recognition (abbr., IDRR) is a NLP task of classifying argument pairs into different types of semantic relations. Arguments contain subtexts, some of which are beneficial to the perception of semantic relations. However, subtexts are connotative. The neural IDRR model fails to be aware of them without being given pertinent prompts. In this paper, we leverage LLaMA to generate subtexts for argument pairs, and verify the effectiveness of subtext-based IDRR. We construct an IDRR baseline using the decoder-only backbone LLaMA, and enhance it with subtext-aware relation reasoning. A confidence-diagnosed dual-channel network is used for collaboration between in-subtext and out-of-subtext IDRR. We experiment on PDTB-2.0 and PDTB-3.0 for both the main-level and secondary-level relation taxonomies. The test results show that our approach yields substantial improvements compared to the baseline, and achieves higher F1-scores on both benchmarks than the previous decoder-only IDRR models. We make the source codes and data publicly available.

The aim of implicit discourse relation recognition is to comprehend the sense of connection between two arguments. In this work, we present a classification method that is solely based on generative models. Our proposed approach employs a combination of instruction templates and in-context learning to refine the generative model for effectively addressing the implicit discourse relation recognition task. Furthermore, we utilize Chain-of-Thoughts to partition the inference process into a sequence of three successive stages. This strategy enables us to fully utilize the autoregressive generative model’s potential for knowledge acquisition and inference, ultimately leading to enhanced performance on this natural language understanding task. The results of our experiments, evaluated on benchmark datasets PDTB 2.0, PDTB 3.0, and the CoNLL16 shared task, demonstrate superior performance compared to previous state-of-the-art models.