When to plummet and when to soar: Corpus based verb selection for natural language generation

Abstract

For data-to-text tasks in Natural Language Generation (NLG), researchers are often faced with choices about the right words to express phenomena seen in the data. One common phenomenon centers around the description of trends between two data points and selecting the appropriate verb to express both the direction and intensity of movement. Our research shows that rather than simply selecting the same verbs again and again, variation and naturalness can be achieved by quantifying writers' patterns of usage around verbs.