3 accepted papers
"In the context of online privacy, many methods propose complex security preserving measures to protect sensitive data. In this paper, we note that: not storing any sensitive data is the best form of security. We propose an online framework called ""Burn After Reading"", i.e. each online sample is p…
We propose, DocQueryNet, a value retrieval method with arbitrary queries for form-like documents to reduce human effort of processing forms. Unlike previous methods that only address a fixed set of field items, our method predicts target value for an arbitrary query based on the understanding of the…
Current contrastive learning frameworks focus on leveraging a single supervisory signal to learn representations, which limits the efficacy on unseen data and downstream tasks. In this paper, we present a hierarchical multi-label representation learning framework that can leverage all available labe…