TASK DEFINITION: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
PROBLEM: We extracted Japanese word pairs from the Evaluation Dataset of Japanese Lexical Simplification kodaira.

SOLUTION: where does the data come from?

PROBLEM: The best baseline model is NO-MOVE, reaching an accuracy of 30.3% on single sentences and 0.3 on complete paragraphs.

SOLUTION: How well did the baseline perform?

PROBLEM: Other Timex3 mentions are more ambiguous so we use a distant supervision approach. Phrases like "currently" and "today's" tend to occur near Events that overlap the current document creation time, while "ago" or " INLINEFORM0 -years" indicate past events. These dominant temporal associations can be learned from training data and then used to label Timex3s. Finally, we define a logistic regression rule to predict entity DocRelTime values as well as specify a linear skip-chain factor over Event mentions and their nearest Timex3 neighbor, encoding the baseline system heuristic directly as an inference rule.

SOLUTION:
How do they obtain distant supervision rules for predicting relations?