# Modeling Content and Context with Deep Relational Learning

arXiv: https://arxiv.org/abs/2010.10453 · HTML: https://mlsanity.com/paper/2010.10453/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 16 of 16)

- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [Identifying Morality Frames in Political Tweets using Relational Learning](https://mlsanity.com/paper/2109.04535/) (2021)
- [A Holistic Framework for Analyzing the COVID-19 Vaccine Debate](https://mlsanity.com/paper/2205.01817/) (2022)
- [Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections](https://mlsanity.com/paper/2305.05094/) (2023)
- [The Effect of Scaling, Retrieval Augmentation and Form on the Factual Consistency of Language Models](https://mlsanity.com/paper/2311.01307/) (2023)
- [Towards Few-Shot Identification of Morality Frames using In-Context Learning](https://mlsanity.com/paper/2302.02029/) (2023)
- [Multi-Task Attentive Residual Networks for Argument Mining](https://mlsanity.com/paper/2102.12227/) (2021)
- [Randomized Deep Structured Prediction for Discourse-Level Processing](https://mlsanity.com/paper/2101.10435/) (2021)
- [Interactively Learning Social Media Representations Improves News Source Factuality Detection](https://mlsanity.com/paper/2309.14966/) (2023)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [The Integer Linear Programming Inference Cookbook](https://mlsanity.com/paper/2307.00171/) (2023)
- [Framing in the Presence of Supporting Data: A Case Study in U.S. Economic News](https://mlsanity.com/paper/2402.14224/) (2024)
- [Mapping the Course for Prompt-based Structured Prediction](https://mlsanity.com/paper/2508.15090/) (2025)
- [Modeling Human Mental States with an Entity-based Narrative Graph](https://mlsanity.com/paper/2104.07079/) (2021)
- [Interactive Topic Models with Optimal Transport](https://mlsanity.com/paper/2406.19928/) (2024)
- [Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study on Vaccination Debate on Social Media](https://mlsanity.com/paper/2502.01991/) (2025)

## Abstract (arXiv)

Building models for realistic natural language tasks requires dealing with long texts and accounting for complicated structural dependencies. Neural-symbolic representations have emerged as a way to combine the reasoning capabilities of symbolic methods, with the expressiveness of neural networks. However, most of the existing frameworks for combining neural and symbolic representations have been designed for classic relational learning tasks that work over a universe of symbolic entities and relations. In this paper, we present DRaiL, an open-source declarative framework for specifying deep relational models, designed to support a variety of NLP scenarios. Our framework supports easy integration with expressive language encoders, and provides an interface to study the interactions between representation, inference and learning.