@inproceedings{sen-etal-2020-learning,
title = "Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification",
author = "Sen, Prithviraj and
Danilevsky, Marina and
Li, Yunyao and
Brahma, Siddhartha and
Boehm, Matthias and
Chiticariu, Laura and
Krishnamurthy, Rajasekar",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.345/",
doi = "10.18653/v1/2020.emnlp-main.345",
pages = "4211--4221",
abstract = "Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance."
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<abstract>Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance.</abstract>
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%0 Conference Proceedings
%T Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification
%A Sen, Prithviraj
%A Danilevsky, Marina
%A Li, Yunyao
%A Brahma, Siddhartha
%A Boehm, Matthias
%A Chiticariu, Laura
%A Krishnamurthy, Rajasekar
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F sen-etal-2020-learning
%X Interpretability of predictive models is becoming increasingly important with growing adoption in the real-world. We present RuleNN, a neural network architecture for learning transparent models for sentence classification. The models are in the form of rules expressed in first-order logic, a dialect with well-defined, human-understandable semantics. More precisely, RuleNN learns linguistic expressions (LE) built on top of predicates extracted using shallow natural language understanding. Our experimental results show that RuleNN outperforms statistical relational learning and other neuro-symbolic methods, and performs comparably with black-box recurrent neural networks. Our user studies confirm that the learned LEs are explainable and capture domain semantics. Moreover, allowing domain experts to modify LEs and instill more domain knowledge leads to human-machine co-creation of models with better performance.
%R 10.18653/v1/2020.emnlp-main.345
%U https://aclanthology.org/2020.emnlp-main.345/
%U https://doi.org/10.18653/v1/2020.emnlp-main.345
%P 4211-4221
Markdown (Informal)
[Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification](https://aclanthology.org/2020.emnlp-main.345/) (Sen et al., EMNLP 2020)
ACL