A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition

• URL: http://arxiv.org/abs/2112.11740v1
• Date: Wed, 22 Dec 2021 09:14:03 GMT
• Title: A Label Dependence-aware Sequence Generation Model for Multi-level Implicit Discourse Relation Recognition
• Authors: Changxing Wu, Liuwen Cao, Yubin Ge, Yang Liu, Min Zhang, Jinsong Su
• Abstract summary: Implicit discourse relation recognition is a challenging but crucial task in discourse analysis. We propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. We develop a mutual learning enhanced training method to exploit the label dependence in a bottomup direction.
• Score: 31.179555215952306
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels independently, while ignoring the dependence between hierarchically structured labels. In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we first design a label attentive encoder to learn the global representation of an input instance and its level-specific contexts, where the label dependence is integrated to obtain better label embeddings. Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level. We further develop a mutual learning enhanced training method to exploit the label dependence in a bottomup direction, which is captured by an auxiliary decoder introduced during training. Experimental results on the PDTB dataset show that our model achieves the state-of-the-art performance on multi-level IDRR. We will release our code at https://github.com/nlpersECJTU/LDSGM.

Related papers

• Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification [11.19022605804112]
  This paper introduces RR2QC, a novel Retrieval Reranking method To multi-label Question Classification. It uses label semantics and meta-label refinement to enhance personalized learning and resource recommendation. Experimental results demonstrate that RR2QC outperforms existing classification methods in Precision@k and F1 scores.
  arXiv  Detail & Related papers  (2024-11-04T06:27:14Z)
• Substituting Data Annotation with Balanced Updates and Collective Loss in Multi-label Text Classification [19.592985329023733]
  Multi-label text classification (MLTC) is the task of assigning multiple labels to a given text. We study the MLTC problem in annotation-free and scarce-annotation settings in which the magnitude of available supervision signals is linear to the number of labels. Our method follows three steps, (1) mapping input text into a set of preliminary label likelihoods by natural language inference using a pre-trained language model, (2) calculating a signed label dependency graph by label descriptions, and (3) updating the preliminary label likelihoods with message passing along the label dependency graph.
  arXiv  Detail & Related papers  (2023-09-24T04:12:52Z)
• Contrastive Label Enhancement [13.628665406039609]
  We propose Contrastive Label Enhancement (ConLE) to generate high-level features by contrastive learning strategy. We leverage the obtained high-level features to gain label distributions through a welldesigned training strategy.
  arXiv  Detail & Related papers  (2023-05-16T14:53:07Z)
• Exploring Structured Semantic Prior for Multi Label Recognition with Incomplete Labels [60.675714333081466]
  Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to compensate for insufficient annotations. We advocate remedying the deficiency of label supervision for the MLR with incomplete labels by deriving a structured semantic prior.
  arXiv  Detail & Related papers  (2023-03-23T12:39:20Z)
• Modeling sequential annotations for sequence labeling with crowds [8.239028141030621]
  Crowd sequential annotations can be an efficient and cost-effective way to build large datasets for sequence labeling. We propose Modeling sequential annotation for sequence labeling with crowds (SA-SLC) A valid label sequence inference (VLSE) method is proposed to derive the valid ground-truth label sequences from crowd sequential annotations.
  arXiv  Detail & Related papers  (2022-09-20T02:51:23Z)
• Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels [86.17081952197788]
  We propose to blend category-specific representation across different images to transfer information of known labels to complement unknown labels. Experiments on the MS-COCO, Visual Genome, Pascal VOC 2007 datasets show that the proposed SARB framework obtains superior performance over current leading competitors.
  arXiv  Detail & Related papers  (2022-03-04T07:56:16Z)
• Structured Semantic Transfer for Multi-Label Recognition with Partial Labels [85.6967666661044]
  We propose a structured semantic transfer (SST) framework that enables training multi-label recognition models with partial labels. The framework consists of two complementary transfer modules that explore within-image and cross-image semantic correlations. Experiments on the Microsoft COCO, Visual Genome and Pascal VOC datasets show that the proposed SST framework obtains superior performance over current state-of-the-art algorithms.
  arXiv  Detail & Related papers  (2021-12-21T02:15:01Z)
• Enhancing Label Correlation Feedback in Multi-Label Text Classification via Multi-Task Learning [6.1538971100140145]
  We introduce a novel approach with multi-task learning to enhance label correlation feedback. We propose two auxiliary label co-occurrence prediction tasks to enhance label correlation learning.
  arXiv  Detail & Related papers  (2021-06-06T12:26:14Z)
• A Study on the Autoregressive and non-Autoregressive Multi-label Learning [77.11075863067131]
  We propose a self-attention based variational encoder-model to extract the label-label and label-feature dependencies jointly. Our model can therefore be used to predict all labels in parallel while still including both label-label and label-feature dependencies.
  arXiv  Detail & Related papers  (2020-12-03T05:41:44Z)
• Knowledge-Guided Multi-Label Few-Shot Learning for General Image Recognition [75.44233392355711]
  KGGR framework exploits prior knowledge of statistical label correlations with deep neural networks. It first builds a structured knowledge graph to correlate different labels based on statistical label co-occurrence. Then, it introduces the label semantics to guide learning semantic-specific features. It exploits a graph propagation network to explore graph node interactions.
  arXiv  Detail & Related papers  (2020-09-20T15:05:29Z)
• Unsupervised Person Re-identification via Multi-label Classification [55.65870468861157]
  This paper formulates unsupervised person ReID as a multi-label classification task to progressively seek true labels. Our method starts by assigning each person image with a single-class label, then evolves to multi-label classification by leveraging the updated ReID model for label prediction. To boost the ReID model training efficiency in multi-label classification, we propose the memory-based multi-label classification loss (MMCL)
  arXiv  Detail & Related papers  (2020-04-20T12:13:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.