Symbolic rule learning methods find regularities in data that can be expressed in the form of 'if-then' rules based on symbolic representations of the data.

An **LSTM** is a type of [recurrent neural network](https://paperswithcode.com/methods/category/recurrent-neural-networks) that addresses the vanishing gradient problem in vanilla RNNs through additional cells, input and output gates. Intuitively, vanishing gradients are solved through additional *additive* components, and forget gate activations, that allow the gradients to flow through the network without vanishing as quickly.

(Image Source [here](https://medium.com/datadriveninvestor/how-do-lstm-networks-solve-the-problem-of-vanishing-gradients-a6784971a577))

(Introduced by Hochreiter and Schmidhuber)

LSTM

Symbolic rule learning

Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs

**Macaw** is a generative question-answering (QA) system that is built on UnifiedQA, itself built on [T5](https://paperswithcode.com/method/t5). Macaw has three interesting features. First, it often produces high-quality answers to questions far outside the domain it was trained on, sometimes surprisingly so. Second, Macaw allows different permutations (“an gles”) of inputs and outputs to be used. For example, we can give it a question and get an answer; or give it an answer and get a question; or give it a question and answer and get a set of multiple-choice (MC) options for that question. This multi-angle QA capability allows versatility in the way Macaw can be used, include recursively using outputs as new inputs to the system. Finally, Macaw also generates explanations as an optional output (or even input) element.

Source	Scalable and interpretable rule-based link prediction for large heterogeneous knowledge graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com