TITLE: A Puzzle-Based Dataset for Natural Language Inference 

ABSTRACT: We provide here a dataset for tasks related to natural language understanding
and natural language inference. The dataset contains logical puzzles in natural
language from three domains: comparing puzzles, knighs and knaves, and zebra
puzzles. Each puzzle is associated with the entire set of atomic questions that
can be generated based on the relations and individuals occurring in the text.
For each question we provide the correct answer: entailment, contradiction or
ambiguity. The answer's correctness is verified against theorem provers. Good
puzzles have two properties: (i) each piece of information is necessary and
(ii) no unnecessary information is provided. These properties make puzzles
interesting candidates for machine comprehension tasks.
We provide here a dataset for tasks related to natural language understanding
and natural language inference. The dataset contains logical puzzles in natural
language from three domains: comparing puzzles, knighs and knaves, and zebra
puzzles. Each puzzle is associated with the entire set of atomic questions that
can be generated based on the relations and individuals occurring in the text.
For each question we provide the correct answer: entailment, contradiction or
ambiguity. The answer's correctness is verified against theorem provers. Good
puzzles have two properties: (i) each piece of information is necessary and
(ii) no unnecessary information is provided. These properties make puzzles
interesting candidates for machine comprehension tasks.
A Puzzle-Based Dataset for Natural Language Inference
http://arxiv.org/abs/2112.05742v1