Multi-Task Active Learning for Linguistic Annotations

By Roi Reichart, Katrin Tomanek, Udo Hahn, Ari Rappoport. 2008

119 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

A Survey on Multi-Task Learning (2017, background): “[120] believe that data instances to be selected”
From Theories to Queries (2011, background): “Along these lines, Reichart et al. (2008) study a two-task active learning scenario for natural language parsing and named entity recognition (NER), a form of information extraction.”
Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner (2012, background): “A typical classification was proposed in [35, 36] where active learning methods improved the overall quality of the learning.”
Multi-task Learning with Labeled and Unlabeled Tasks (2016, background): “Exemplary works in this direction are (Reichart et al., 2008; Saha et al., 2010; Gavves et al., 2015), which, however, use active learning on the level of training examples, not tasks.”
Multi-Task Learning for Dense Prediction Tasks: A Survey (2020, background): “This includes various domains, such as language [24], [98], [99], audio [100], video [101], [102], and robotics [103], [104], as well as with different learning paradigms, such as reinforcement learning [105], [106], self-supervised learning [107], semisupervised learning [108], [109] and active lea”
Conditionally Adaptive Multi-Task Learning: Improving Transfer Learning in NLP Using Fewer Parameters & Less Data (2020, methodology): “Reichart et al. (2008) has examined different active learning techniques for the two task annotation scenario, focusing on named entity recognition and syntactic parse tree annotations.”
The strategic student approach for life-long exploration and learning (2012, methodology): “A typical classification task was considered on the works of [41], [42] where active learning methods are used to improve not only one task, but the overall quality of the different tasks.”
Heterogeneous Transfer Learning for Image Clustering via the SocialWeb (2009, background): “, 2007; Chan and Ng, 2007; Daume, 2007; Jiang and Zhai, 2007; Reichart and Rappoport, 2007; Andreevskaia and Bergler, 2008), multi-task learning (Caruana, 1997; Reichart et al., 2008; Arnold et al., 2008), self-taught learning (Raina et al.”
A survey of adaptive sampling for global metamodeling in support of simulation-based complex engineering design (2017, background): “To this end, Reichart et al. (2008) investigated two sampling strategies for two-response problems.”
Active Multi-task Learning via Bandits (2015, background): “[28] considered the active learning algorithm for multi-task problem in linguistic annotations and combined rankings/scores from all single-task active learnings.”