Sitemap

About Me

Improving Implicit Discourse Relation Recognition via Connective Prediction and Dependency-weighted Label Hierarchy

Published in 2024 International Joint Conference on Neural Networks (IJCNN), 2024

Implicit discourse relation recognition aims to identify logical relations between two arguments without explicit connectives and is a challenging task in discourse analysis. Recent methods tend to leverage the label hierarchy to enhance discourse relation representations. However, they fail to fully utilize the connective information. Specifically, the methods overlook the guiding role of connectives in discourse relation classification by treating them as the last-level labels in the label hierarchy to leverage connective information, whereas it would be more appropriate to exploit connective information prior to relation classification. Moreover, these methods ignore the dependency degree of labels between different levels in the label hierarchy. In other words, they consider the label hierarchy as an unweighted undirected graph, and assume that the path weights between high-level labels and their corresponding low-level labels are the same, which leads to an insufficient construction of the label hierarchy. To overcome these issues, we propose a method for implicit discourse relation recognition (IDRR) utilizing Connective Prediction and Dependency-weighted Label Hierarchy (CP-DLH). Experimental results on PDTB 2.0 dataset show that our model achieves the state-of-the-art performance at all hierarchical levels.

Recommended citation: Liu X, Guo S, Li J, Su X, Ma B, et al. Improving Implicit Discourse Relation Recognition via Connective Prediction and Dependency-weighted Label Hierarchy[C]//2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024: 1-8.
Download Paper

Multi-Granularity Dual-Aware Contrastive Learning for Few-shot Named Entity Recognition

Published in 2024 International Joint Conference on Neural Networks (IJCNN), 2024

Few-shot Named Entity Recognition aims to identify named entities from unstructured texts in various domains using a minimal amount of training samples and classify them into predefined categories. Many popular approaches decompose this process into two tasks: Span Detection and Entity Classification. However, they still have some issues: (1) Neglecting presentation optimization. Most of these methods emphasize classification, neglecting the optimization of span presentation during Span Detection. (2) Missing label semantics. They have not fully leveraged semantic information of entity type labels during Entity Classification. To address these issues, this paper proposes Multi-Granularity Dual-Aware Contrastive Learning (MGDAC) for few-shot NER. Specifically, we introduce multi-granularity contrastive learning for solve neglecting presentation optimization, focusing on both the overall vector and internal vector granularity to enhance features beneficial for span detection in token vector representations. Additionally, we design dual-aware contrastive learning for solve missing label semantics, effectively utilizing semantic information from both entity tokens and entity type labels during prototype construction to jointly optimize prototype representations. Finally, extensive experiments on the FewNERD dataset demonstrate that our proposed method exhibits improvements in both Span Detection and Entity Classification, outperforming other competitive baseline methods.

Recommended citation: Ma B, Wang C, Guo S, et al. Multi-Granularity Dual-Aware Contrastive Learning for Few-shot Named Entity Recognition[C]//2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024: 1-8.
Download Paper

Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution

Published in Agriculture, 2024

Semantic segmentation of three-dimensional (3D) plant point clouds at the stem-leaf level is foundational and indispensable for high-throughput tomato phenotyping systems. However, existing semantic segmentation methods often suffer from issues such as low precision and slow inference speed. To address these challenges, we propose an innovative encoding-decoding structure, incorporating voxel sparse convolution (SpConv) and attention-based feature fusion (VSCAFF) to enhance semantic segmentation of the point clouds of high-resolution tomato seedling images. Tomato seedling point clouds from the Pheno4D dataset labeled into semantic classes of ‘leaf’, ‘stem’, and ‘soil’ are applied for the semantic segmentation. In order to reduce the number of parameters so as to further improve the inference speed, the SpConv module is designed to function through the residual concatenation of the skeleton convolution kernel and the regular convolution kernel. The feature fusion module based on the attention mechanism is designed by giving the corresponding attention weights to the voxel diffusion features and the point features in order to avoid the ambiguity of points with different semantics having the same characteristics caused by the diffusion module, in addition to suppressing noise. Finally, to solve model training class bias caused by the uneven distribution of point cloud classes, the composite loss function of Lovász-Softmax and weighted cross-entropy is introduced to supervise the model training and improve its performance. The results show that mIoU of VSCAFF is 86.96%, which outperformed the performance of PointNet, PointNet++, and DGCNN, respectively. IoU of VSCAFF achieves 99.63% in the soil class, 64.47% in the stem class, and 96.72% in the leaf class. The time delay of 35ms in inference speed is better than PointNet++ and DGCNN. The results demonstrate that VSCAFF has high performance and inference speed for semantic segmentation of high-resolution tomato point clouds, and can provide technical support for the high-throughput automatic phenotypic analysis of tomato plants.

Recommended citation: Li S, Yan Z, Ma B, et al. Semantic Segmentation Method for High-Resolution Tomato Seedling Point Clouds Based on Sparse Convolution[J]. Agriculture, 2024, 15(1): 74.
Download Paper

Memorization ≠ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?

Published in Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025

Driven by vast and diverse textual data, large language models (LLMs) have demonstrated impressive performance across numerous natural language processing (NLP) tasks. Yet, a critical question persists: does their generalization arise from mere memorization of training data or from deep semantic understanding? To investigate this, we propose a bi-perspective evaluation framework to assess LLMs’ scenario cognition—the ability to link semantic scenario elements with their arguments in context. Specifically, we introduce a novel scenario-based dataset comprising diverse textual descriptions of fictional facts, annotated with scenario elements. LLMs are evaluated through their capacity to answer scenario-related questions (model output perspective) and via probing their internal representations for encoded scenario elements-argument associations (internal representation perspective). Our experiments reveal that current LLMs predominantly rely on superficial memorization, failing to achieve robust semantic scenario cognition, even in simple cases. These findings expose critical limitations in LLMs’ semantic understanding and offer cognitive insights for advancing their capabilities.

Recommended citation: Ma B, Li R, Yuanlong W, et al. Memorization≠ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?[C]//Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025: 20758-20774.
Download Paper

Oral - EMNLP 2025

Published: November 06, 2025

Oral for the accepted paper: Memorization ≠ Understanding: Do Large Language Models Have the Ability of Scenario Cognition?