Multi-level implicit discourse relation recognition (MIDRR) aims at identifying hierarchical discourse relations among arguments.Previous methods achieve the promotion through finetuning PLMs.However, due to the data scarcity and the task gap, the pre-trained feature space cannot be accurately tuned to the task-specific space, which even aggravates the collapse of the vanilla space.Besides, the comprehension of hierarchical semantics for MIDRR makes the conversion much harder.In this paper, we propose a prompt-based Parameter-Efficient Multilevel IDRR (PEMI) framework to solve the above problems.First, we leverage parameterefficient prompt tuning to drive the inputted arguments to match the pre-trained space and realize the approximation with few parameters.Furthermore, we propose a hierarchical label refining (HLR) method for the prompt verbalizer to deeply integrate hierarchical guidance into the prompt tuning.Finally, our model achieves comparable results on PDTB 2.0 and 3.0 using about 0.1% trainable parameters compared with baselines and the visualization demonstrates the effectiveness of our HLR method.

The long-tailed distribution, also called the heavy-tailed distribution, is common in nature. Since both words and their senses in natural language have long-tailed phenomenon in usage frequency, the Word Sense Disambiguation (WSD) task faces serious data imbalance. The existing learning strategies or data augmentation methods are difficult to deal with the lack of training samples caused by the single application scenario of long-tail senses, and the word sense representations caused by unique word sense definitions. Considering that the features extracted from the Disentangled Representation (DR) independently describe the essential properties of things, and DR does not require deep feature extraction and fusion processes, it alleviates the dependence of the representation learning on the training samples. We propose a novel DR by constraining the covariance matrix of a multivariate Gaussian distribution, which can enhance the strength of independence among features compared to β-VAE. The WSD model implemented by the reinforced DR outperforms the baselines on the English all-words WSD evaluation framework, the constructed long-tail word sense datasets, and the latest cross-lingual datasets.