High Quality Embeddings for Horn Logic Reasoning

This paper introduces novel approaches for creating high-quality embeddings for logical statements, crucial for training neural networks to efficiently rank choices made by logical reasoners. These methods involve generating anchors with repeated terms, balancing easy, medium, and hard examples for triplet loss training, and periodically emphasizing the hardest examples.