  We study the problem of quantitative facts extraction for text with
percentages. For example, given the sentence "30 percent of Americans like
watching football, while 20% prefer to watch NBA.", our goal is to obtain a
deep understanding of the percentage numbers ("30 percent" and "20%") by
extracting their quantitative facts: part ("like watching football" and "prefer
to watch NBA") and whole ("Americans). These quantitative facts can empower new
applications like automated infographic generation. We formulate part and whole
extraction as a sequence tagging problem. Due to the large gap between
part/whole and its corresponding percentage, we introduce skip mechanism in
sequence modeling, and achieved improved performance on both our task and the
CoNLL-2003 named entity recognition task. Experimental results demonstrate that
learning to skip in sequence tagging is promising.
