Enhancing Label Correlation Feedback in Multi-Label Text Classification
via Multi-Task Learning
- URL: http://arxiv.org/abs/2106.03103v1
- Date: Sun, 6 Jun 2021 12:26:14 GMT
- Title: Enhancing Label Correlation Feedback in Multi-Label Text Classification
via Multi-Task Learning
- Authors: Ximing Zhang, Qian-Wen Zhang, Zhao Yan, Ruifang Liu and Yunbo Cao
- Abstract summary: 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.
- Score: 6.1538971100140145
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In multi-label text classification (MLTC), each given document is associated
with a set of correlated labels. To capture label correlations, previous
classifier-chain and sequence-to-sequence models transform MLTC to a sequence
prediction task. However, they tend to suffer from label order dependency,
label combination over-fitting and error propagation problems. To address these
problems, we introduce a novel approach with multi-task learning to enhance
label correlation feedback. We first utilize a joint embedding (JE) mechanism
to obtain the text and label representation simultaneously. In MLTC task, a
document-label cross attention (CA) mechanism is adopted to generate a more
discriminative document representation. Furthermore, we propose two auxiliary
label co-occurrence prediction tasks to enhance label correlation learning: 1)
Pairwise Label Co-occurrence Prediction (PLCP), and 2) Conditional Label
Co-occurrence Prediction (CLCP). Experimental results on AAPD and RCV1-V2
datasets show that our method outperforms competitive baselines by a large
margin. We analyze low-frequency label performance, label dependency, label
combination diversity and coverage speed to show the effectiveness of our
proposed method on label correlation learning.
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) - Label Dependencies-aware Set Prediction Networks for Multi-label Text Classification [0.0]
We leverage Graph Convolutional Networks and construct an adjacency matrix based on the statistical relations between labels.
We enhance recall ability by applying the Bhattacharyya distance to the output distributions of the set prediction networks.
arXiv Detail & Related papers (2023-04-14T09:31:17Z) - 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) - Complementary to Multiple Labels: A Correlation-Aware Correction
Approach [65.59584909436259]
We show theoretically how the estimated transition matrix in multi-class CLL could be distorted in multi-labeled cases.
We propose a two-step method to estimate the transition matrix from candidate labels.
arXiv Detail & Related papers (2023-02-25T04:48:48Z) - Group is better than individual: Exploiting Label Topologies and Label
Relations for Joint Multiple Intent Detection and Slot Filling [39.76268402567324]
We construct a Heterogeneous Label Graph (HLG) containing two kinds of topologies.
Label correlations are leveraged to enhance semantic-label interactions.
We also propose the label-aware inter-dependent decoding mechanism to further exploit the label correlations for decoding.
arXiv Detail & Related papers (2022-10-19T08:21:43Z) - Group-aware Label Transfer for Domain Adaptive Person Re-identification [179.816105255584]
Unsupervised Adaptive Domain (UDA) person re-identification (ReID) aims at adapting the model trained on a labeled source-domain dataset to a target-domain dataset without any further annotations.
Most successful UDA-ReID approaches combine clustering-based pseudo-label prediction with representation learning and perform the two steps in an alternating fashion.
We propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
arXiv Detail & Related papers (2021-03-23T07:57:39Z) - 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) - Evolving Multi-label Classification Rules by Exploiting High-order Label
Correlation [2.9822184411723645]
In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously.
The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations.
This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system.
arXiv Detail & Related papers (2020-07-22T18:13:12Z) - Few-shot Slot Tagging with Collapsed Dependency Transfer and
Label-enhanced Task-adaptive Projection Network [61.94394163309688]
We propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model -- TapNet.
Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting.
arXiv Detail & Related papers (2020-06-10T07:50:44Z) - Interaction Matching for Long-Tail Multi-Label Classification [57.262792333593644]
We present an elegant and effective approach for addressing limitations in existing multi-label classification models.
By performing soft n-gram interaction matching, we match labels with natural language descriptions.
arXiv Detail & Related papers (2020-05-18T15:27:55Z)
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.