Paper ID: 2203.15099

LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Models

Santiago Ontanon, Joshua Ainslie, Vaclav Cvicek, Zachary Fisher

Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference. Although many datasets exist to evaluate compositional generalization, when it comes to evaluating inference abilities, options are more limited. This paper presents LogicInference, a new dataset to evaluate the ability of models to perform logical inference. The dataset focuses on inference using propositional logic and a small subset of first-order logic, represented both in semi-formal logical notation, as well as in natural language. We also report initial results using a collection of machine learning models to establish an initial baseline in this dataset.

Submitted: Mar 28, 2022