Agent-Specific Deontic Modality Detection in Legal Language

Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel Rudinger

Abstract:

Legal documents are typically long and written
in legalese, which makes it particularly difficult for laypeople to understand their rights
and duties. While natural language understanding technologies can be valuable in supporting
such understanding in the legal domain, the
limited availability of datasets annotated for deontic modalities in the legal domain, due to the
cost of hiring experts and privacy issues, is a
bottleneck. To this end, we introduce, LEXDEMOD, a corpus of English contracts annotated
with deontic modality expressed with respect
to a contracting party or agent along with the
modal triggers. We benchmark this dataset on
two tasks: (i) agent-specific multi-label deontic
modality classification, and (ii) agent-specific
deontic modality and trigger span detection using Transformer-based (Vaswani et al., 2017)
language models. Transfer learning experiments show that the linguistic diversity of
modal expressions in LEXDEMOD generalizes reasonably from lease to employment and
rental agreements. A small case study indicates
that a model trained on LEXDEMOD can detect red flags with high recall. We believe our
work offers a new research direction for deontic
modality detection in the legal domain1