LogicInference: A New Dataset for Teaching Logical Inference to seq2seq Models
Autor: | Ontanon, Santiago, Ainslie, Joshua, Cvicek, Vaclav, Fisher, Zachary |
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Rok vydání: | 2022 |
Předmět: | |
Druh dokumentu: | Working Paper |
Popis: | 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. Comment: Accepted at ICLR 2022 OSC workshop (v3 contains updated results after fixing a problem in dataset generation) |
Databáze: | arXiv |
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