Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition

• URL: http://arxiv.org/abs/2210.07916v1
• Date: Fri, 14 Oct 2022 16:02:03 GMT
• Title: Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition
• Authors: Shuguang Chen, Leonardo Neves, Thamar Solorio
• Abstract summary: We propose a new method to transform the text from a high-resource domain to a low-resource domain by changing its style-related attributes. We design a constrained decoding algorithm along with a set of key ingredients for data selection to guarantee the generation of valid and coherent data. Our approach is a practical solution to data scarcity, and we expect it to be applicable to other NLP tasks.
• Score: 17.892385961143173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we take the named entity recognition task in the English language as a case study and explore style transfer as a data augmentation method to increase the size and diversity of training data in low-resource scenarios. We propose a new method to effectively transform the text from a high-resource domain to a low-resource domain by changing its style-related attributes to generate synthetic data for training. Moreover, we design a constrained decoding algorithm along with a set of key ingredients for data selection to guarantee the generation of valid and coherent data. Experiments and analysis on five different domain pairs under different data regimes demonstrate that our approach can significantly improve results compared to current state-of-the-art data augmentation methods. Our approach is a practical solution to data scarcity, and we expect it to be applicable to other NLP tasks.

Related papers

• Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition [1.2878987353423252]
  Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR) We introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples.
  arXiv  Detail & Related papers  (2024-10-13T16:40:48Z)
• A Simple Background Augmentation Method for Object Detection with Diffusion Model [53.32935683257045]
  In computer vision, it is well-known that a lack of data diversity will impair model performance. We propose a simple yet effective data augmentation approach by leveraging advancements in generative models. Background augmentation, in particular, significantly improves the models' robustness and generalization capabilities.
  arXiv  Detail & Related papers  (2024-08-01T07:40:00Z)
• Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning [114.54944761345594]
  We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods. Our proposed method aims to improve performance in multi-task training, zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2023-08-10T03:09:12Z)
• Augmentation Invariant Manifold Learning [0.5827521884806071]
  We introduce a new representation learning method called augmentation invariant manifold learning. Compared with existing self-supervised methods, the new method simultaneously exploits the manifold's geometric structure and invariant property of augmented data. Our theoretical investigation characterizes the role of data augmentation in the proposed method and reveals why and how the data representation learned from augmented data can improve the $k$-nearest neighbor in the downstream analysis.
  arXiv  Detail & Related papers  (2022-11-01T13:42:44Z)
• Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations [76.82124752950148]
  We develop a convenient gradient-based method for selecting the data augmentation. We use a differentiable Kronecker-factored Laplace approximation to the marginal likelihood as our objective.
  arXiv  Detail & Related papers  (2022-02-22T02:51:11Z)
• Consistency and Diversity induced Human Motion Segmentation [231.36289425663702]
  We propose a novel Consistency and Diversity induced human Motion (CDMS) algorithm. Our model factorizes the source and target data into distinct multi-layer feature spaces. A multi-mutual learning strategy is carried out to reduce the domain gap between the source and target data.
  arXiv  Detail & Related papers  (2022-02-10T06:23:56Z)
• Implicit Data Augmentation Using Feature Interpolation for Diversified Low-Shot Image Generation [11.4559888429977]
  Training of generative models can easily diverge in low-data setting. We propose a novel implicit data augmentation approach which facilitates stable training and synthesize diverse samples.
  arXiv  Detail & Related papers  (2021-12-04T23:55:46Z)
• Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data [77.88594632644347]
  Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization challenge. We propose a novel momentum-based method to mitigate this decentralized training difficulty.
  arXiv  Detail & Related papers  (2021-02-09T11:27:14Z)
• Flexible deep transfer learning by separate feature embeddings and manifold alignment [0.0]
  Object recognition is a key enabler across industry and defense. Unfortunately, algorithms trained on existing labeled datasets do not directly generalize to new data because the data distributions do not match. We propose a novel deep learning framework that overcomes this limitation by learning separate feature extractions for each domain.
  arXiv  Detail & Related papers  (2020-12-22T19:24:44Z)
• Propositionalization and Embeddings: Two Sides of the Same Coin [0.0]
  This paper outlines some of the modern data processing techniques used in relational learning. It focuses on the propositionalization and embedding data transformation approaches. We present two efficient implementations of the unifying methodology.
  arXiv  Detail & Related papers  (2020-06-08T08:33:21Z)
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer [64.22926988297685]
  Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP) In this paper, we explore the landscape of introducing transfer learning techniques for NLP by a unified framework that converts all text-based language problems into a text-to-text format.
  arXiv  Detail & Related papers  (2019-10-23T17:37:36Z)

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.