| Challenge: | Existing paradigms for training neural networks require large datasets, a paper argues . we present a framework for introducing declarative knowledge to neural networks . |
| Approach: | They propose a framework for introducing declarative knowledge to neural networks . they compile logical statements into graphs that augment a network without extra learnable parameters or manual redesign. |
| Outcome: | The proposed framework improves on three tasks, especially in low-data regimes. |
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| Challenge: | Neural networks are notoriously data-hungry, resulting in ungrammatical texts . data augmentation requires a specific design for a structurally rich input format . |
| Approach: | They propose to selectively augment a training set with new data by adding and varying two specific lexical categories, i.e. proper and common nouns. |
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Logical Neural Networks for Knowledge Base Completion with Embeddings & Rules (2022.emnlp-main)
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Prithviraj Sen, Breno William Carvalho, Ibrahim Abdelaziz, Pavan Kapanipathi, Salim Roukos, Alexander Gray
| Challenge: | Knowledge base completion (KBC) is a human-interpretable dialect . rule-based KBC has a high quality but low accuracy . |
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Learning beyond Datasets: Knowledge Graph Augmented Neural Networks for Natural Language Processing (N18-1)
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| Challenge: | Currently, machine learning is limited in scalability and is limited to specific training data. |
| Approach: | They propose to enhance learning models with world knowledge in the form of Knowledge Graph fact triples for natural language processing tasks. |
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Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completion (2023.acl-short)
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| Challenge: | Recent studies show that high-quality rule sets struggle with high coverage. |
| Approach: | They propose three simple augmentations to existing rule sets to improve results . they propose transforming rules to their abductive forms and generating equivalent rules that use inverse forms of constituent relations . |
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Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
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Learning Explainable Linguistic Expressions with Neural Inductive Logic Programming for Sentence Classification (2020.emnlp-main)
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Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy
| Challenge: | Existing approaches to explain models are difficult to interpret and have undesirable biases. |
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Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
| Approach: | They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences. |
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Order Doesn’t Matter, But Reasoning Does: Training LLMs with Order-Centric Augmentation (2025.emnlp-main)
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| Challenge: | Logical reasoning is essential for large language models (LLMs) to ensure accurate and coherent inferences. |
| Approach: | They propose an order-centric data augmentation framework based on commutativity in logical reasoning that randomly shuffles independent premises to introduce condition order augmentation. |
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Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis (2024.lrec-main)
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| Challenge: | Existing methods for inductive knowledge graph completion are underperforming . implausible entities are not ranked and only the most informative path is taken into account . |
| Approach: | They propose to use a rule-based approach to find plausible triples missing from a given KG. |
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Neural Unification for Logic Reasoning over Natural Language (2021.findings-emnlp)
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| Challenge: | Automated Theorem Proving (ATP) is a computer program that can show that conjectures are logical consequences of a set of axioms. |
| Approach: | They propose a transformer-based architecture for deriving conjectures given axioms . they propose 'neural unifier' and relative training procedure to train the model . |
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