Training Dynamic based data filtering may not work for NLP datasets

• URL: http://arxiv.org/abs/2109.09191v1
• Date: Sun, 19 Sep 2021 18:50:45 GMT
• Title: Training Dynamic based data filtering may not work for NLP datasets
• Authors: Arka Talukdar, Monika Dagar, Prachi Gupta, Varun Menon
• Abstract summary: We study the applicability of the Area Under the Margin (AUM) metric to identify mislabelled examples in NLP datasets. We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent increase in dataset size has brought about significant advances in natural language understanding. These large datasets are usually collected through automation (search engines or web crawlers) or crowdsourcing which inherently introduces incorrectly labeled data. Training on these datasets leads to memorization and poor generalization. Thus, it is pertinent to develop techniques that help in the identification and isolation of mislabelled data. In this paper, we study the applicability of the Area Under the Margin (AUM) metric to identify and remove/rectify mislabelled examples in NLP datasets. We find that mislabelled samples can be filtered using the AUM metric in NLP datasets but it also removes a significant number of correctly labeled points and leads to the loss of a large amount of relevant language information. We show that models rely on the distributional information instead of relying on syntactic and semantic representations.

Related papers

• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Deep Active Learning with Manifold-preserving Trajectory Sampling [2.0717982775472206]
  Active learning (AL) is for optimizing the selection of unlabeled data for annotation (labeling) Existing deep AL methods arguably suffer from bias incurred by clabeled data, which takes a much lower percentage than unlabeled data in AL context. We propose a novel method, namely Manifold-Preserving Trajectory Sampling (MPTS), aiming to enforce the feature space learned from labeled data to represent a more accurate manifold.
  arXiv  Detail & Related papers  (2024-10-21T03:04:09Z)
• Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling [6.771578432805963]
  Active learning aims to alleviate the amount of labor involved in data labeling by automating the selection of unlabeled samples. We introduce novel techniques that significantly improve the use of abundant unlabeled data during training. We demonstrate the superior performance of our approach over the state of the art on various image classification and segmentation benchmark datasets.
  arXiv  Detail & Related papers  (2024-08-23T00:35:07Z)
• Automatic Dataset Construction (ADC): Sample Collection, Data Curation, and Beyond [38.89457061559469]
  We propose an innovative methodology that automates dataset creation with negligible cost and high efficiency. We provide open-source software that incorporates existing methods for label error detection, robust learning under noisy and biased data. We design three benchmark datasets focused on label noise detection, label noise learning, and class-imbalanced learning.
  arXiv  Detail & Related papers  (2024-08-21T04:45:12Z)
• Self-Supervised Learning for User Localization [8.529237718266042]
  Machine learning techniques have shown remarkable accuracy in localization tasks. Their dependency on vast amounts of labeled data, particularly Channel State Information (CSI) and corresponding coordinates, remains a bottleneck. We propose a pioneering approach that leverages self-supervised pretraining on unlabeled data to boost the performance of supervised learning for user localization based on CSI.
  arXiv  Detail & Related papers  (2024-04-19T21:49:10Z)
• Maximizing Data Efficiency for Cross-Lingual TTS Adaptation by Self-Supervised Representation Mixing and Embedding Initialization [57.38123229553157]
  This paper presents an effective transfer learning framework for language adaptation in text-to-speech systems. We focus on achieving language adaptation using minimal labeled and unlabeled data. Experimental results show that our framework is able to synthesize intelligible speech in unseen languages with only 4 utterances of labeled data and 15 minutes of unlabeled data.
  arXiv  Detail & Related papers  (2024-01-23T21:55:34Z)
• FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning [73.13448439554497]
  Semi-Supervised Learning (SSL) has been an effective way to leverage abundant unlabeled data with extremely scarce labeled data. Most SSL methods are commonly based on instance-wise consistency between different data transformations. We propose FlatMatch which minimizes a cross-sharpness measure to ensure consistent learning performance between the two datasets.
  arXiv  Detail & Related papers  (2023-10-25T06:57:59Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification. XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• Are labels informative in semi-supervised learning? -- Estimating and leveraging the missing-data mechanism [4.675583319625962]
  Semi-supervised learning is a powerful technique for leveraging unlabeled data to improve machine learning models. It can be affected by the presence of informative'' labels, which occur when some classes are more likely to be labeled than others. We propose a novel approach to address this issue by estimating the missing-data mechanism and using inverse propensity weighting to debias any SSL algorithm.
  arXiv  Detail & Related papers  (2023-02-15T09:18:46Z)
• Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density [58.64907136562178]
  We study the effectiveness of Feature Density (FD) using different linguistically-backed feature preprocessing methods. We hypothesise that estimating dataset complexity allows for the reduction of the number of required experiments. The difference in linguistic complexity of datasets allows us to additionally discuss the efficacy of linguistically-backed word preprocessing.
  arXiv  Detail & Related papers  (2021-11-02T15:48:28Z)
• Self-Tuning for Data-Efficient Deep Learning [75.34320911480008]
  Self-Tuning is a novel approach to enable data-efficient deep learning. It unifies the exploration of labeled and unlabeled data and the transfer of a pre-trained model. It outperforms its SSL and TL counterparts on five tasks by sharp margins.
  arXiv  Detail & Related papers  (2021-02-25T14:56:19Z)

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.