awesome-deeplogic

awesome-deeplogic

A collection of papers of neural-symbolic AI (mainly focus on NLP applications)

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Awesome deep logic is a curated list of papers and resources focusing on integrating symbolic logic into deep neural networks. It includes surveys, tutorials, and research papers that explore the intersection of logic and deep learning. The repository aims to provide valuable insights and knowledge on how logic can be used to enhance reasoning, knowledge regularization, weak supervision, and explainability in neural networks.

README:

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

Survey and Open Challenges

  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]

Tutorials

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

Logic as Knowledge Regularization

  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]

Logic as Weak Supervision

  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]

Incorporate Logic into NN Module

  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]

Explainability and Understanding

  1. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models, Mihir Parmar et,al ACL 2024 [pdf]

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

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

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

Related Application

  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]

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