  Contract element extraction (CEE) is the novel task of automatically
identifying and extracting legally relevant elements such as contract dates,
payments, and legislation references from contracts. Automatic methods for this
task view it as a sequence labeling problem and dramatically reduce human
labor. However, as contract genres and element types may vary widely, a
significant challenge for this sequence labeling task is how to transfer
knowledge from one domain to another, i.e., cross-domain CEE. Cross-domain CEE
differs from cross-domain named entity recognition (NER) in two important ways.
First, contract elements are far more fine-grained than named entities, which
hinders the transfer of extractors. Second, the extraction zones for
cross-domain CEE are much larger than for cross-domain NER. As a result, the
contexts of elements from different domains can be more diverse. We propose a
framework, the Bi-directional Feedback cLause-Element relaTion network
(Bi-FLEET), for the cross-domain CEE task that addresses the above challenges.
Bi-FLEET has three main components: (1) a context encoder, (2) a clause-element
relation encoder, and (3) an inference layer. To incorporate invariant
knowledge about element and clause types, a clause-element graph is constructed
across domains and a hierarchical graph neural network is adopted in the
clause-element relation encoder. To reduce the influence of context variations,
a multi-task framework with a bi-directional feedback scheme is designed in the
inference layer, conducting both clause classification and element extraction.
The experimental results over both cross-domain NER and CEE tasks show that
Bi-FLEET significantly outperforms state-of-the-art baselines.
