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    <abstract>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.</abstract>
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%0 Journal Article
%T Modeling Content and Context with Deep Relational Learning
%A Pacheco, Maria Leonor
%A Goldwasser, Dan
%J Transactions of the Association for Computational Linguistics
%D 2021
%V 9
%I MIT Press
%C Cambridge, MA
%F pacheco-goldwasser-2021-modeling
%X 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.
%R 10.1162/tacl_a_00357
%U https://aclanthology.org/2021.tacl-1.7
%U https://doi.org/10.1162/tacl_a_00357
%P 100-119

Markdown (Informal)

[Modeling Content and Context with Deep Relational Learning](https://aclanthology.org/2021.tacl-1.7) (Pacheco & Goldwasser, TACL 2021)

• Modeling Content and Context with Deep Relational Learning (Pacheco & Goldwasser, TACL 2021)

ACL

• Maria Leonor Pacheco and Dan Goldwasser. 2021. Modeling Content and Context with Deep Relational Learning. Transactions of the Association for Computational Linguistics, 9:100–119.