Monotonicity Entailment Dataset
MED is a new evaluation dataset that covers a wide range of monotonicity reasoning that was created by crowdsourcing and collected from linguistics publications. The dataset was constructed by collecting naturally-occurring examples by crowdsourcing and well-designed ones from linguistics publications.
It consists of 5,382 examples.
Source: https://github.com/verypluming/MED
Image Source: https://www.aclweb.org/anthology/W19-4804v2.pdf
Variants: MED
This dataset is used in 1 benchmark:
| Task | Model | Paper | Date |
|---|---|---|---|
| Natural Language Inference | NeuralLog | NeuralLog: Natural Language Inference with … | 2021-05-29 |
Recent papers with results on this dataset: