Weak, Indirect and Self Supervision for Knowledge Extraction (WISE Supervision)

Wenpeng Yin, Muhao Chen, Lifu Huang, Huan Sun, Hongming Zhang, Benjamin Roth, Barbara Plank

Abstract: Knowledge extraction (KE) was mainly driven by task-specific human annotations. Recent years have seen an increasing interest in KE with WISE supervision (Weak supervision, Indirect supervision, SElf-supervision, etc.). This workshop aims to provide a forum for researchers and practitioners from broad communities, such as information extraction (IE), knowledge graphs (KG), semantic web, and transfer learning, etc., to discuss the challenges and promises of KE when human annotations are limited. Wise-Supervision 2022 aims to bring together researchers from different areas related to KE. As such, the workshop welcomes and covers a wide range of topics, including (non-exclusively): IE/KE with indirect supervision from textual entailment, summarization, etc. IE/KE with weak supervision and denoising. IE/KE with self-supervision, e.g., pretrained LMs for IE/KE. KG construction and consolidation. Low-resource IE/KE. KE in industry settings.