Committee-Based Active Learning for Dependency Parsing

Majidi, Saeed; Crane, Gregory

doi:10.1007/978-3-642-40501-3_56

Cited by 5 publications

(5 citation statements)

References 4 publications

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“…Moreover, selftraining can be especially helpful for low-resource scenarios, such as in few-shot learning (Vu et al, 2021;. Self-training has also been a commonly adopted strategy to enhance active learning (Tomanek and Hahn, 2009;Majidi and Crane, 2013;Yu et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…It is particularly compatible with PA-based AL since the un-selected substructures are typically also highly-confident under the current model and likely to be predicted correctly without requiring additional annotation. We revisit this idea from previous work (Tomanek and Hahn, 2009;Majidi and Crane, 2013) and investigate its applicability with modern neural models and our adaptive partial selection approach.…”

Section: Introductionmentioning

confidence: 99%

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Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

Zhang,

Strubell,

Hovy

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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In this work we propose a pragmatic method that reduces the annotation cost for structured label spaces using active learning. Our approach leverages partial annotation, which reduces labeling costs for structured outputs by selecting only the most informative substructures for annotation. We also utilize selftraining to incorporate the current model's automatic predictions as pseudo-labels for unannotated sub-structures. A key challenge in effectively combining partial annotation with self-training to reduce annotation cost is determining which sub-structures to select to label. To address this challenge, we adopt an error estimator to adaptively decide the partial selection ratio according to the current model's capability. In evaluations spanning four structured prediction tasks, we show that our combination of partial annotation and self-training using an adaptive selection ratio reduces annotation cost over strong full annotation baselines under a fair comparison scheme that takes reading time into consideration.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

Zhang,

Strubell,

Hovy

2023

Findings of the Association for Computational Linguistics: EMNLP 2023

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“…The first is "complete-then-train" (Mirroshandel and Nasr, 2011; Majidi and Crane, 2013), which "completes" every partially annotated de-pendency parse by finding the most likely parse (according to an already trained parser model) that respects the constraints of the partial annotations. These "completed" parses are then used to train a new parser.…”

Section: Learning From Partial Annotationmentioning

confidence: 99%

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples

Joshi¹,

Peters²,

Hopkins³

2018

Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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We revisit domain adaptation for parsers in the neural era. First we show that recent advances in word representations greatly diminish the need for domain adaptation when the target domain is syntactically similar to the source domain. As evidence, we train a parser on the Wall Street Journal alone that achieves over 90% F 1 on the Brown corpus. For more syntactically distant domains, we provide a simple way to adapt a parser using only dozens of partial annotations. For instance, we increase the percentage of error-free geometry-domain parses in a held-out set from 45% to 73% using approximately five dozen training examples. In the process, we demonstrate a new state-of-the-art single model result on the Wall Street Journal test set of 94.3%. This is an absolute increase of 1.7% over the previous state-of-the-art of 92.6%.

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“…Another common sampling strategy is based on the reduction of version space, among which query-by-committee (QBC) algorithm is the most popular one. QBC algorithms train a committee of classifiers and choose the instance on which the committee members most disagree [ 9 ]. In essence, the QBC is also based on uncertainty sampling.…”

Section: Related Workmentioning

confidence: 99%

An Active Learning Approach with Uncertainty, Representativeness, and Diversity

Zhang

Xin

et al. 2014

The Scientific World Journal

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Big data from the Internet of Things may create big challenge for data classification. Most active learning approaches select either uncertain or representative unlabeled instances to query their labels. Although several active learning algorithms have been proposed to combine the two criteria for query selection, they are usually ad hoc in finding unlabeled instances that are both informative and representative and fail to take the diversity of instances into account. We address this challenge by presenting a new active learning framework which considers uncertainty, representativeness, and diversity creation. The proposed approach provides a systematic way for measuring and combining the uncertainty, representativeness, and diversity of an instance. Firstly, use instances' uncertainty and representativeness to constitute the most informative set. Then, use the kernel k-means clustering algorithm to filter the redundant samples and the resulting samples are queried for labels. Extensive experimental results show that the proposed approach outperforms several state-of-the-art active learning approaches.

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Committee-Based Active Learning for Dependency Parsing

Cited by 5 publications

References 4 publications

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training

Extending a Parser to Distant Domains Using a Few Dozen Partially Annotated Examples

An Active Learning Approach with Uncertainty, Representativeness, and Diversity

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