Must-Read Papers or Resources on how to integrate symbolic logic into deep neural nets.

1. From Machine Learning to Machine Reasoning Leon Bottou Arxiv 2011 [pdf]

2. From Statistical Relational to Neuro-Symbolic Artificial Intelligence Luc De Raedt , Sebastijan Dumanˇci ́c , Robin Manhaeve and Giuseppe Marra Arxiv 2020 [pdf]

3. Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective Luis C. Lamb et,al. Arxiv 2020 [pdf]

4. Relational inductive biases, deep learning and graph networks Peter W. Battaglia et,al. Arxiv 2018 [pdf]

1. Neuro-Symbolic Methods For Language And Vision AAAI 2022 [link]

1. Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logic Xufeng Zhao, Mengdi Li, Wenhao Lu, Cornelius Weber, Jae Hee Lee, Kun Chu, Stefan Wermter. COLING 2024 [pdf] [code]

2. Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference. Yichao Zhou, Yu Yan, Rujun Han, J. Harry Caufield,Kai-Wei Chang, Yizhou Sun, Peipei Ping and Wei Wang. AAAI 2021 [pdf] [code]

3. Integrating Deep Learning with Logic Fusion for Information Extraction. Wenya Wang, Sinno Jialin Pan. AAAI 2020 [pdf] [code]

4. Logic-guided Data Augmentation and Reguralization for Consistent Question Answering. Akari Asai, Hannaneh Hajishirzi. ACL 2020 [pdf] [code]

5. Structured Tuning for Semantic Role Labeling. Tao Li, Parth Anand Jawale, Martha Palmer, Vivek Srikumar ACL 2020 [pdf] [code]

6. Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection. Ruize Wang, Duyu Tang, et,al EMNLP 2020 [pdf]

7. Joint Constrained Learning for Event-Event Relation Extraction Haoyu Wang, Muhao Chen, Hongming Zhang, Dan Roth EMNLP 2020 [pdf]

8. A Logic-Driven Framework for Consistency of Neural Models. Tao Li, Vivek Gupta, Maitrey Mehta, Vivek Srikumar EMNLP-IJCNLP 2019 [pdf] [code]

9. Adversarially regularising neural NLI models to integrate logical background knowledge. Pasquale Minervini, Sebastian Riedel. CoNLL 2018 [pdf] [code]

10. Lifted Rule Injection for Relation Embeddings. Thomas Demeester, Tim Rocktäschel, Sebastian Riedel EMNLP 2016 [pdf]

1. Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference Yufei Feng, Xiaoyu Yang, Xiaodan Zhu, Michael Greenspan TACL 2022 [pdf]

2. LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking Hang Jiang et,al ACL 2021 [pdf]

3. Weakly Supervised Named Entity Tagging with Learnable Logical Rules. Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, Zhe Feng ACL-IJCNLP 2021 [pdf] [code]

1. Learning Language Representations with Logical Inductive Bias. Jianshu Chen ICLR 2023 pdf]

2. Modeling Content and Context with Deep Relational Learning Maria Leonor Pacheco and Dan Goldwasser TACL 2021 [pdf] [code]

3. Logical Neural Networks Ryan Riegel et,al (IBM Research) Arxiv 2020 [pdf]

1. Transformers Implement First-Order Logic with Majority Quantifiers William Merrill, Ashish Sabharwal Arxiv 2022 [pdf]

2. What Can Neural Networks Reson About? Keyulu Xu, Jingling Li et,al ICLR 2020 [pdf] [code]

3. Relational Reasoning and Generalization using Non-symbolic Neural Networks Arxiv 2020 [pdf]

1. Complex Query Answering With Neural Link Predictors Erik Arakelyan, Daniel Daza, Pasquale Minervini & Michael Cochez ICLR 2021 [pdf] [code]

2. Faithfully Explainable Recommendation via Neural Logic Reasoning Yaxin Zhu, Yikun Xian, Zuohui Fu, Gerard de Melo, Yongfeng Zhang NAACL [pdf] [code]

3. Correlating neural and symbolic representations of language. Grzegorz Chrupała, Afra Alishahi. ACL 2019 [pdf][code]

4. Representing Meaning with a Combination of Logical and Distributional Models. I. Beltagy, Stephen Roller, Pengxiang Cheng, Katrin Erk, Raymond J. Mooney. Computational Linguistics 2016 [pdf] [code]