  Span extraction, aiming to extract text spans (such as words or phrases) from
plain texts, is a fundamental process in Information Extraction. Recent works
introduce the label knowledge to enhance the text representation by formalizing
the span extraction task into a question answering problem (QA Formalization),
which achieves state-of-the-art performance. However, QA Formalization does not
fully exploit the label knowledge and suffers from low efficiency in
training/inference. To address those problems, we introduce a new paradigm to
integrate label knowledge and further propose a novel model to explicitly and
efficiently integrate label knowledge into text representations. Specifically,
it encodes texts and label annotations independently and then integrates label
knowledge into text representation with an elaborate-designed semantics fusion
module. We conduct extensive experiments on three typical span extraction
tasks: flat NER, nested NER, and event detection. The empirical results show
that 1) our method achieves state-of-the-art performance on four benchmarks,
and 2) reduces training time and inference time by 76% and 77% on average,
respectively, compared with the QA Formalization paradigm. Our code and data
are available at https://github.com/Akeepers/LEAR.
