# Obligation and Prohibition Extraction Using Hierarchical RNNs

arXiv: https://arxiv.org/abs/1805.03871 · HTML: https://mlsanity.com/paper/1805.03871/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2018.

## Built on by (most cited first, 18 of 18)

- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [Multi-LexSum: Real-World Summaries of Civil Rights Lawsuits at Multiple Granularities](https://mlsanity.com/paper/2206.10883/) (2022)
- [MAUD: An Expert-Annotated Legal NLP Dataset for Merger Agreement Understanding](https://mlsanity.com/paper/2301.00876/) (2023)
- [Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer](https://mlsanity.com/paper/2211.00974/) (2022)
- [Multi-granular Legal Topic Classification on Greek Legislation](https://mlsanity.com/paper/2109.15298/) (2021)
- [Agent-Specific Deontic Modality Detection in Legal Language](https://mlsanity.com/paper/2211.12752/) (2022)
- [Thinking Longer, Not Always Smarter: Evaluating LLM Capabilities in Hierarchical Legal Reasoning](https://mlsanity.com/paper/2510.08710/) (2025)
- [Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code](https://mlsanity.com/paper/2112.03033/) (2021)
- [What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://mlsanity.com/paper/2212.09825/) (2022)
- [RIRAG: Regulatory Information Retrieval and Answer Generation](https://mlsanity.com/paper/2409.05677/) (2024)
- [Deontological Keyword Bias: The Impact of Modal Expressions on Normative Judgments of Language Models](https://mlsanity.com/paper/2506.11068/) (2025)
- [Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models](https://mlsanity.com/paper/2210.13086/) (2022)
- [DeepParliament: A Legal domain Benchmark & Dataset for Parliament Bills Prediction](https://mlsanity.com/paper/2211.15424/) (2022)
- [Extracting Norms from Contracts Via ChatGPT: Opportunities and Challenges](https://mlsanity.com/paper/2404.02269/) (2024)
- [Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary](https://mlsanity.com/paper/2504.04131/) (2025)
- [Computational Identification of Regulatory Statements in EU Legislation](https://mlsanity.com/paper/2505.00479/) (2025)
- [Streamlining Industrial Contract Management with Retrieval-Augmented LLMs](https://mlsanity.com/paper/2511.14671/) (2025)

## Abstract (arXiv)

We consider the task of detecting contractual obligations and prohibitions. We show that a self-attention mechanism improves the performance of a BILSTM classifier, the previous state of the art for this task, by allowing it to focus on indicative tokens. We also introduce a hierarchical BILSTM, which converts each sentence to an embedding, and processes the sentence embeddings to classify each sentence. Apart from being faster to train, the hierarchical BILSTM outperforms the flat one, even when the latter considers surrounding sentences, because the hierarchical model has a broader discourse view.