Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data

Wang, Yaqing; Xu, Yifan; Li, Xian; Dong, Xin Luna; Gao, Jing

doi:10.1145/3394486.3403303

Cited by 10 publications

(3 citation statements)

References 21 publications

(22 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meta-learning has long been proposed as a form of learning that would allow systems to systematically build up and re-use knowledge across different but related tasks [34,36,37]. MAML [6] is to learn model initialization parameters that are used to rapidly learn novel tasks with a small set of labeled data.…”

Section: Few-shot Learningmentioning

confidence: 99%

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

Wang

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

View full text Add to dashboard Cite

Fake news travels at unprecedented speeds, reaches global audiences and puts users and communities at great risk via social media platforms. Deep learning based models show good performance when trained on large amounts of labeled data on events of interest, whereas the performance of models tends to degrade on other events due to domain shift. Therefore, significant challenges are posed for existing detection approaches to detect fake news on emergent events, where large-scale labeled datasets are difficult to obtain. Moreover, adding the knowledge from newly emergent events requires to build a new model from scratch or continue to fine-tune the model, which can be challenging, expensive, and unrealistic for real-world settings. In order to address those challenges, we propose an end-to-end fake news detection framework named MetaFEND, which is able to learn quickly to detect fake news on emergent events with a few verified posts. Specifically, the proposed model integrates meta-learning and neural process methods together to enjoy the benefits of these approaches. In particular, a label embedding module and a hard attention mechanism are proposed to enhance the effectiveness by handling categorical information and trimming irrelevant posts. Extensive experiments are conducted on multimedia datasets collected from Twitter and Weibo. The experimental results show our proposed MetaFEND model can detect fake news on never-seen events effectively and outperform the state-of-the-art methods. CCS CONCEPTS• Computing methodologies → Artificial intelligence; • Information systems → Web applications.

show abstract

Section: Few-shot Learningmentioning

confidence: 99%

Multimodal Emergent Fake News Detection via Meta Neural Process Networks

Wang

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

View full text Add to dashboard Cite

show abstract

“…The MAG team used machine curation to solve the problem of uniquely identifying authors and their publications (Wang et.al 2020). A human curation strategy advocates setting up standards such as Document Object Identifier (DOI) for uniquely identifying publications, and Open Researcher and Contributor ID (ORCID) for uniquely identifying authors.…”

Section: Contrasting Perspectivesmentioning

confidence: 99%

“…The next level of scaling aimed to reduce modeling cost through AutoML and automatic cleaning techniques (Wang et al. 2020) so that manual tuning for each knowledge extraction model could be significantly reduced. Scaling further required reducing the total number of models required for the variety of knowledge to be extracted, which was achieved through transfer learning techniques such that a model can extract knowledge for multiple attributes and for multiple domains (Karamanolakis, Ma, and Dong 2020).…”

Section: Contrasting Perspectivesmentioning

confidence: 99%

Knowledge graphs: Introduction, history, and perspectives

et al. 2022

Self Cite

View full text Add to dashboard Cite

Knowledge graphs (KGs) have emerged as a compelling abstraction for organizing the world's structured knowledge and for integrating information extracted from multiple data sources. They are also beginning to play a central role in representing information extracted by AI systems, and for improving the predictions of AI systems by giving them knowledge expressed in KGs as input. The goals of this article are to (a) introduce KGs and discuss important areas of application that have gained recent prominence; (b) situate KGs in the context of the prior work in AI; and (c) present a few contrasting perspectives that help in better understanding KGs in relation to related technologies.

show abstract