Adaptive Active Learning for Coreference Resolution
- URL: http://arxiv.org/abs/2104.07611v1
- Date: Thu, 15 Apr 2021 17:21:51 GMT
- Title: Adaptive Active Learning for Coreference Resolution
- Authors: Michelle Yuan, Patrick Xia, Benjamin Van Durme, Jordan Boyd-Graber
- Abstract summary: Recent developments in incremental coreference resolution allow for a novel approach to active learning in this setting.
By lowering the data barrier for coreference, coreference resolvers can rapidly adapt to a series of previously unconsidered domains.
- Score: 37.261220564076964
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Training coreference resolution models require comprehensively labeled data.
A model trained on one dataset may not successfully transfer to new domains.
This paper investigates an approach to active learning for coreference
resolution that feeds discrete annotations to an incremental clustering model.
The recent developments in incremental coreference resolution allow for a novel
approach to active learning in this setting. Through this new framework, we
analyze important factors in data acquisition, like sources of model
uncertainty and balancing reading and labeling costs. We explore different
settings through simulated labeling with gold data. By lowering the data
barrier for coreference, coreference resolvers can rapidly adapt to a series of
previously unconsidered domains.
Related papers
- Data Adaptive Traceback for Vision-Language Foundation Models in Image Classification [34.37262622415682]
We propose a new adaptation framework called Data Adaptive Traceback.
Specifically, we utilize a zero-shot-based method to extract the most downstream task-related subset of the pre-training data.
We adopt a pseudo-label-based semi-supervised technique to reuse the pre-training images and a vision-language contrastive learning method to address the confirmation bias issue in semi-supervised learning.
arXiv Detail & Related papers (2024-07-11T18:01:58Z) - Uncertainty-guided Open-Set Source-Free Unsupervised Domain Adaptation with Target-private Class Segregation [22.474866164542302]
UDA approaches commonly assume that source and target domains share the same labels space.
This paper considers the more challenging Source-Free Open-set Domain Adaptation (SF-OSDA) setting.
We propose a novel approach for SF-OSDA that exploits the granularity of target-private categories by segregating their samples into multiple unknown classes.
arXiv Detail & Related papers (2024-04-16T13:52:00Z) - Overcoming Overconfidence for Active Learning [1.2776312584227847]
We present two novel methods to address the problem of overconfidence that arises in the active learning scenario.
The first is an augmentation strategy named Cross-Mix-and-Mix (CMaM), which aims to calibrate the model by expanding the limited training distribution.
The second is a selection strategy named Ranked Margin Sampling (RankedMS), which prevents choosing data that leads to overly confident predictions.
arXiv Detail & Related papers (2023-08-21T09:04:54Z) - Explaining Cross-Domain Recognition with Interpretable Deep Classifier [100.63114424262234]
Interpretable Deep (IDC) learns the nearest source samples of a target sample as evidence upon which the classifier makes the decision.
Our IDC leads to a more explainable model with almost no accuracy degradation and effectively calibrates classification for optimum reject options.
arXiv Detail & Related papers (2022-11-15T15:58:56Z) - A self-training framework for glaucoma grading in OCT B-scans [6.382852973055393]
We present a self-training-based framework for glaucoma grading using OCT B-scans under the presence of domain shift.
A two-step learning methodology resorts to pseudo-labels generated during the first step to augment the training dataset on the target domain.
We propose a novel glaucoma-specific backbone which introduces residual and attention modules via skip-connections to refine the embedding features of the latent space.
arXiv Detail & Related papers (2021-11-23T10:33:55Z) - On Generalization in Coreference Resolution [66.05112218880907]
We consolidate a set of 8 coreference resolution datasets targeting different domains to evaluate the off-the-shelf performance of models.
We then mix three datasets for training; even though their domain, annotation guidelines, and metadata differ, we propose a method for jointly training a single model.
We find that in a zero-shot setting, models trained on a single dataset transfer poorly while joint training yields improved overall performance.
arXiv Detail & Related papers (2021-09-20T16:33:22Z) - A Curriculum-style Self-training Approach for Source-Free Semantic Segmentation [91.13472029666312]
We propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation.
Our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions.
arXiv Detail & Related papers (2021-06-22T10:21:39Z) - $n$-Reference Transfer Learning for Saliency Prediction [73.17061116358036]
We propose a few-shot transfer learning paradigm for saliency prediction.
The proposed framework is gradient-based and model-agnostic.
The results show that the proposed framework achieves a significant performance improvement.
arXiv Detail & Related papers (2020-07-09T23:20:44Z) - Active Learning for Coreference Resolution using Discrete Annotation [76.36423696634584]
We improve upon pairwise annotation for active learning in coreference resolution.
We ask annotators to identify mention antecedents if a presented mention pair is deemed not coreferent.
In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour.
arXiv Detail & Related papers (2020-04-28T17:17:11Z)
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.