2 research outputs found
Automatic Validation of Textual Attribute Values in E-commerce Catalog by Learning with Limited Labeled Data
Product catalogs are valuable resources for eCommerce website. In the
catalog, a product is associated with multiple attributes whose values are
short texts, such as product name, brand, functionality and flavor. Usually
individual retailers self-report these key values, and thus the catalog
information unavoidably contains noisy facts. Although existing deep neural
network models have shown success in conducting cross-checking between two
pieces of texts, their success has to be dependent upon a large set of quality
labeled data, which are hard to obtain in this validation task: products span a
variety of categories. To address the aforementioned challenges, we propose a
novel meta-learning latent variable approach, called MetaBridge, which can
learn transferable knowledge from a subset of categories with limited labeled
data and capture the uncertainty of never-seen categories with unlabeled data.
More specifically, we make the following contributions. (1) We formalize the
problem of validating the textual attribute values of products from a variety
of categories as a natural language inference task in the few-shot learning
setting, and propose a meta-learning latent variable model to jointly process
the signals obtained from product profiles and textual attribute values. (2) We
propose to integrate meta learning and latent variable in a unified model to
effectively capture the uncertainty of various categories. (3) We propose a
novel objective function based on latent variable model in the few-shot
learning setting, which ensures distribution consistency between unlabeled and
labeled data and prevents overfitting by sampling from the learned
distribution. Extensive experiments on real eCommerce datasets from hundreds of
categories demonstrate the effectiveness of MetaBridge on textual attribute
validation and its outstanding performance compared with state-of-the-art
approaches.Comment: KDD 202
Multimodal Emergent Fake News Detection via Meta Neural Process Networks
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.Comment: accepted by KDD 202