Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Schröder, Christopher; Annanias, Yves; Niekler, Andreas; Müller, Lydia; Wiegreffe, Daniel; Bender, Christian; Mengs, Christoph; Scheuermann, Gerik; Heyer, Gerhard

doi:10.18653/v1/2021.acl-long.320

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…Hence, we should augment the representative cases that reflect a larger set of their properties (representative), instead of some noisy samples that do not represent the general attribute of the whole dataset. This is also inspired by a previous study (Schröder and Niekler 2020), which shows that training on representative cases can increase the quality of the resulting model.…”

Section: Introductionmentioning

confidence: 84%

Learning towards Selective Data Augmentation for Dialogue Generation

Chen

Zhang

et al. 2023

AAAI

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As it is cumbersome and expensive to acquire a huge amount of data for training neural dialog models, data augmentation is proposed to effectively utilize existing training samples. However, current data augmentation techniques on the dialog generation task mostly augment all cases in the training dataset without considering the intrinsic attributes between different cases. We argue that not all cases are beneficial for augmentation task, and the cases suitable for augmentation should obey the following two attributes: (1) low-quality (the dialog model cannot generate a high-quality response for the case), (2) representative (the case should represent the property of the whole dataset). Herein, we explore this idea by proposing a Selective Data Augmentation framework (SDA) for the response generation task. SDA employs a dual adversarial network to select the lowest quality and most representative data points for augmentation in one stage. Extensive experiments conducted on two publicly available datasets, i.e., DailyDialog and OpenSubtitles, show that our framework can improve the response generation performance with respect to various metrics

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Section: Introductionmentioning

confidence: 84%

Learning towards Selective Data Augmentation for Dialogue Generation

Chen

Zhang

et al. 2023

AAAI

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“…4) Explanatory Annotations: These provide a hard or soft label along with an explanation for each annotation. For example, Schröder et al [82] use topic-related annotations for environmental texts. Similarly, Yan et al [83] annotate the text and list keywords as evidence of the accuracy of the label.…”

Section: Taxonomy Of Dalmentioning

confidence: 99%

“…As shown in Table III, the integration of DL and AL is leading to an increasing application of AL methods in various domains of life, ranging from agricultural development [82] to industrial revitalization [82] and from artificial intelligence [137] to biomedical fields [160]. In this section, we aim to provide a systematic and detailed overview of existing DAL-related work from a broad application perspective.…”

Section: Applications Of Dalmentioning

confidence: 99%

A Survey on Deep Active Learning: Recent Advances and New Frontiers

Li,

Wang,

Chen

et al. 2024

IEEE Trans. Neural Netw. Learning Syst.

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Active learning seeks to achieve strong performance with fewer training samples. It does this by iteratively asking an oracle to label newly selected samples in a human-in-the-loop manner. This technique has gained increasing popularity due to its broad applicability, yet its survey papers, especially for deep active learning (DAL), remain scarce. Therefore, we conduct an advanced and comprehensive survey on DAL. We first introduce reviewed paper collection and filtering. Second, we formally define the DAL task and summarize the most influential baselines and widely used datasets. Third, we systematically provide a taxonomy of DAL methods from five perspectives, including annotation types, query strategies, deep model architectures, learning paradigms, and training processes, and objectively analyze their strengths and weaknesses. Then, we comprehensively summarize the main applications of DAL in natural language processing (NLP), computer vision (CV), data mining (DM), and so on. Finally, we discuss challenges and perspectives after a detailed analysis of current studies. This work aims to serve as a useful and quick guide for researchers in overcoming difficulties in DAL. We hope that this survey will spur further progress in this burgeoning field.

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“…where Class Coverage It is also of interest to consider how many classes AL can find (Schröder et al, 2021;Wertz et al, 2022). Achieving a high or even full class coverage is desirable for several reasons.…”

Section: Measures From User-centric Perspectivementioning

confidence: 99%

Mind the User! Measures to More Accurately Evaluate the Practical Value of Active Learning Strategies

Romberg

2023

Proceedings of the Conference Recent Advances in Natural Language Processing - Large Language Models for Natural Language Proce

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One solution to limited annotation budgets is active learning (AL), a collaborative process of human and machine to strategically select a small but informative set of examples. While current measures optimize AL from a pure machine learning perspective, we argue that for a successful transfer into practice, additional criteria must target the second pillar of AL, the human annotator. In text classification, e.g., where practitioners regularly encounter datasets with an increased number of imbalanced classes, measures like F 1 fall short when finding all classes or identifying rare cases is required. We therefore introduce four measures that reflect class-related demands that users place on data acquisition. In a comprehensive comparison of uncertainty-based, diversitybased, and hybrid query strategies on six different datasets, we find that strong F 1 performance is not necessarily associated with full class coverage. Uncertainty sampling outperforms diversity sampling in selecting minority classes and covering classes more efficiently, while diversity sampling excels in selecting less monotonous batches. Our empirical findings emphasize that a holistic view is essential when evaluating AL approaches to ensure their usefulness in practice -the actual, but often overlooked, goal of development. To this end, standard measures for assessing the performance of text classification need to be complemented by such that more appropriately reflect user needs.

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Supporting Land Reuse of Former Open Pit Mining Sites using Text Classification and Active Learning

Cited by 6 publications

References 18 publications

Learning towards Selective Data Augmentation for Dialogue Generation

Learning towards Selective Data Augmentation for Dialogue Generation

A Survey on Deep Active Learning: Recent Advances and New Frontiers

Mind the User! Measures to More Accurately Evaluate the Practical Value of Active Learning Strategies

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