# Agent-Specific Deontic Modality Detection in Legal Language

arXiv: https://arxiv.org/abs/2211.12752 · HTML: https://mlsanity.com/paper/2211.12752/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 17 of its 66 references that are in the map.
- First posted on arXiv 23 Nov 2022.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019)
- [JEC-QA: A Legal-Domain Question Answering Dataset](https://mlsanity.com/paper/1911.12011/) (2019)
- [Learning to Predict Charges for Criminal Cases with Legal Basis](https://mlsanity.com/paper/1707.09168/) (2017)
- [Charge-Based Prison Term Prediction with Deep Gating Network](https://mlsanity.com/paper/1908.11521/) (2019)
- [LexNLP: Natural language processing and information extraction for legal and regulatory texts](https://mlsanity.com/paper/1806.03688/) (2018)
- [Plain English Summarization of Contracts](https://mlsanity.com/paper/1906.00424/) (2019)
- [CJRC: A Reliable Human-Annotated Benchmark DataSet for Chinese Judicial Reading Comprehension](https://mlsanity.com/paper/1912.09156/) (2019)
- [A Benchmark for Lease Contract Review](https://mlsanity.com/paper/2010.10386/) (2020)
- [Obligation and Prohibition Extraction Using Hierarchical RNNs](https://mlsanity.com/paper/1805.03871/) (2018)
- [The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing](https://mlsanity.com/paper/2106.08037/) (2021)
- [Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events](https://mlsanity.com/paper/2004.04295/) (2020)
- [Entailment Relation Aware Paraphrase Generation](https://mlsanity.com/paper/2203.10483/) (2022)

## Built on by (most cited first, 7 of 7)

- [A Survey of Classification Tasks and Approaches for Legal Contracts](https://mlsanity.com/paper/2507.21108/) (2025)
- [What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://mlsanity.com/paper/2212.09825/) (2022)
- [Dealing with Data for RE: Mitigating Challenges while using NLP and Generative AI](https://mlsanity.com/paper/2402.16977/) (2024)
- [Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification](https://mlsanity.com/paper/2508.07849/) (2025)
- [ComplianceNLP: Knowledge-Graph-Augmented RAG for Multi-Framework Regulatory Gap Detection](https://mlsanity.com/paper/2604.23585/) (2026)
- [Computational Identification of Regulatory Statements in EU Legislation](https://mlsanity.com/paper/2505.00479/) (2025)
- [PolicyKG: An Agentic LLM Pipeline for Translating Institutional Policies into SHACL Knowledge Graphs](https://mlsanity.com/paper/2608.09028/) (2026)

## Abstract (arXiv)

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 domain.