Data Programming for Learning Discourse Structure

Badene, Sonia; Thompson, Kate; Lorré, Jean-Pierre; Asher, Nicholas

doi:10.18653/v1/p19-1061

Cited by 16 publications

(9 citation statements)

References 20 publications

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“…GWASkb with thousands of genotype-phenotype associations was created by using Snorkel in (Kuleshov et al, 2019). Snorkel was also used for chemical reaction relationship extraction (Mallory et al, 2020), discourse structure learning (Badene et al, 2019) and medical entity classification (Fries et al, 2020).…”

Section: Weakly Supervised Learningmentioning

confidence: 99%

Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

Jia¹,

Shen²,

Tang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Prerequisite relations among concepts are crucial for educational applications, such as curriculum planning and intelligent tutoring. In this paper, we propose a novel concept prerequisite relation learning approach, named CPRL, which combines both concept representation learned from a heterogeneous graph and concept pairwise features. Furthermore, we extend CPRL under weakly supervised settings to make our method more practical, including learning prerequisite relations from learning object dependencies and generating training data with data programming. Our experiments on four datasets show that the proposed approach achieves the state-of-the-art results comparing with existing methods.

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Section: Weakly Supervised Learningmentioning

confidence: 99%

Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

Jia¹,

Shen²,

Tang³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…Such work includes examples from the medical domain (Callahan et al, 2019;Dutta and Saha, 2019;Dutta et al, 2020;Saab et al, 2019Saab et al, , 2020, multi-task learning (Ratner et al, 2018(Ratner et al, , 2019a, information extraction (Ehrenberg et al, 2016), and learning discourse structure (Badene et al, 2019). Like our work, such work often adjusts the Snorkel framework (Ratner et al, 2017) for the task at hand.…”

Section: Related Workmentioning

confidence: 99%

Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation

Winkler¹,

Brugman²,

Berkel³

et al. 2021

Proceedings of the 4th Workshop on E-Commerce and NLP

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We carry out a case study on the use of data programming to create data to train classifiers used for product moderation on a large e-commerce platform. Data programming is a recently-introduced technique that uses human-defined rules to generate training data sets without tedious item-by-item hand labeling. Our study investigates methods for allowing product moderators to quickly modify the rules given their knowledge of the domain and, especially, of textual item descriptions. Our results show promise that moderators can use this approach to steer the training data, making possible fast and close control of classifiers that detect policy violations.

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“…Weakly-supervised text classification (WTC) aims to use various weakly supervised signals to do text classification. Weak supervision signals used by existing methods includes external knowledge base (Gabrilovich et al, 2007;Chang et al, 2008;Song and Roth, 2014;Yin et al, 2019), keywords (Agichtein and Gravano, 2000;Riloff et al, 2003;Kuipers et al, 2006;Tao et al, 2015;Meng et al, 2018Meng et al, , 2019Meng et al, , 2020Mekala and Shang, 2020;Wang et al, 2021;Shen et al, 2021) and heuristics rules (Ratner et al, 2016(Ratner et al, , 2017Badene et al, 2019;Shu et al, 2020). In this paper, we focus on keyword-driven methods.…”

Section: Weakly-supervised Text Classificationmentioning

confidence: 99%

Weakly-supervised Text Classification Based on Keyword Graph

Zhang¹,

Ding²,

Xu³

et al. 2021

Preprint

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Weakly-supervised text classification has received much attention in recent years for it can alleviate the heavy burden of annotating massive data. Among them, keyword-driven methods are the mainstream where user-provided keywords are exploited to generate pseudolabels for unlabeled texts. However, existing methods treat keywords independently, thus ignore the correlation among them, which should be useful if properly exploited. In this paper, we propose a novel framework called ClassKG to explore keyword-keyword correlation on keyword graph by GNN. Our framework is an iterative process. In each iteration, we first construct a keyword graph, so the task of assigning pseudo labels is transformed to annotating keyword subgraphs. To improve the annotation quality, we introduce a self-supervised task to pretrain a subgraph annotator, and then finetune it. With the pseudo labels generated by the subgraph annotator, we then train a text classifier to classify the unlabeled texts. Finally, we re-extract keywords from the classified texts. Extensive experiments on both long-text and short-text datasets show that our method substantially outperforms the existing ones.

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Data Programming for Learning Discourse Structure

Cited by 16 publications

References 20 publications

Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data

Exploring Inspiration Sets in a Data Programming Pipeline for Product Moderation

Weakly-supervised Text Classification Based on Keyword Graph

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