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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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tags: |
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- sequence classification |
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- formal languages |
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- regular languages |
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- long-distance dependencies |
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- logical complexity |
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- generalization |
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pretty_name: MLRegTest |
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size_categories: |
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- 10K<n<100K |
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--- |
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The dataset is stored at the OSF [here](https://osf.io/ksdnm/) |
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MLRegTest is a benchmark for sequence classification, containing training, development, and test sets from 1,800 regular languages. |
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Regular languages are formal languages, which are sets of sequences definable with certain kinds of formal grammars, including |
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regular expressions, finite-state acceptors, and monadic second-order logic with either the successor or precedence relation in the |
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model signature for words. This benchmark was designed to help identify those factors, specifically the kinds of long-distance |
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dependencies, that can make it difficult for ML systems to generalize successfully in learning patterns over sequences. MLRegTest |
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organizes its languages according to their logical complexity (monadic second-order, first-order, propositional, or monomial |
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expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity |
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and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages, |
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and therefore to understand the capabilities of different ML systems to learn such long-distance dependencies. The authors think it |
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will be an important milestone if other researchers are able to find an ML system that succeeds across the board on MLRegTest. |
