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