SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup

• URL: http://arxiv.org/abs/2010.02322v1
• Date: Mon, 5 Oct 2020 20:27:14 GMT
• Title: SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
• Authors: Rongzhi Zhang, Yue Yu and Chao Zhang
• Abstract summary: We propose a simple but effective data augmentation method to improve the label efficiency of active sequence labeling. Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration. In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples.
• Score: 11.606681893887604
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Active learning is an important technique for low-resource sequence labeling tasks. However, current active sequence labeling methods use the queried samples alone in each iteration, which is an inefficient way of leveraging human annotations. We propose a simple but effective data augmentation method to improve the label efficiency of active sequence labeling. Our method, SeqMix, simply augments the queried samples by generating extra labeled sequences in each iteration. The key difficulty is to generate plausible sequences along with token-level labels. In SeqMix, we address this challenge by performing mixup for both sequences and token-level labels of the queried samples. Furthermore, we design a discriminator during sequence mixup, which judges whether the generated sequences are plausible or not. Our experiments on Named Entity Recognition and Event Detection tasks show that SeqMix can improve the standard active sequence labeling method by $2.27\%$--$3.75\%$ in terms of $F_1$ scores. The code and data for SeqMix can be found at https://github.com/rz-zhang/SeqMix

Related papers

• Extracting Clean and Balanced Subset for Noisy Long-tailed Classification [66.47809135771698]
  We develop a novel pseudo labeling method using class prototypes from the perspective of distribution matching. By setting a manually-specific probability measure, we can reduce the side-effects of noisy and long-tailed data simultaneously. Our method can extract this class-balanced subset with clean labels, which brings effective performance gains for long-tailed classification with label noise.
  arXiv  Detail & Related papers  (2024-04-10T07:34:37Z)
• Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm [73.94839250910977]
  Noisy multi-label learning has garnered increasing attention due to the challenges posed by collecting large-scale accurate labels. The introduction of transition matrices can help model multi-label noise and enable the development of statistically consistent algorithms. We propose a novel estimator that leverages label correlations without the need for anchor points or precise fitting of noisy class posteriors.
  arXiv  Detail & Related papers  (2023-09-22T08:35:38Z)
• Drawing the Same Bounding Box Twice? Coping Noisy Annotations in Object Detection with Repeated Labels [6.872072177648135]
  We propose a novel localization algorithm that adapts well-established ground truth estimation methods. Our algorithm also shows superior performance during training on the TexBiG dataset.
  arXiv  Detail & Related papers  (2023-09-18T13:08:44Z)
• OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification [9.990725102725916]
  Extreme multi-label text classification (XMTC) is the task of finding the most relevant subset labels from a large-scale label collection. We propose an autoregressive sequence-to-set model for XMTC tasks named OTSeq2Set. Our model generates predictions in student-forcing scheme and is trained by a loss function based on bipartite matching.
  arXiv  Detail & Related papers  (2022-10-26T07:25:18Z)
• 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)
• Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models [105.4590533269863]
  We propose AutoSeq, a fully automatic prompting method. We adopt natural language prompts on sequence-to-sequence models. Our method reveals the potential of sequence-to-sequence models in few-shot learning.
  arXiv  Detail & Related papers  (2022-09-20T01:35:04Z)
• Multi-label Classification with High-rank and High-order Label Correlations [62.39748565407201]
  Previous methods capture the high-order label correlations mainly by transforming the label matrix to a latent label space with low-rank matrix factorization. We propose a simple yet effective method to depict the high-order label correlations explicitly, and at the same time maintain the high-rank of the label matrix. Comparative studies over twelve benchmark data sets validate the effectiveness of the proposed algorithm in multi-label classification.
  arXiv  Detail & Related papers  (2022-07-09T05:15:31Z)
• 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)
• Accelerating BERT Inference for Sequence Labeling via Early-Exit [65.7292767360083]
  We extend the recent successful early-exit mechanism to accelerate the inference of PTMs for sequence labeling tasks. We also propose a token-level early-exit mechanism that allows partial tokens to exit early at different layers. Our approach can save up to 66%-75% inference cost with minimal performance degradation.
  arXiv  Detail & Related papers  (2021-05-28T14:39:26Z)
• Semantic Label Smoothing for Sequence to Sequence Problems [54.758974840974425]
  We propose a technique that smooths over emphwell formed relevant sequences that have sufficient n-gram overlap with the target sequence. Our method shows a consistent and significant improvement over the state-of-the-art techniques on different datasets.
  arXiv  Detail & Related papers  (2020-10-15T00:31:15Z)

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.