Exploring Diversification In Non-factoid Question Answering

Abstract

Retrieving short, precise answers to non-factoid queries is an increasingly important task, especially for mobile and voice search. Many of these questions may have multiple or alternative answers. In an environment where answers are presented incrementally, this raises the question of how to generate a diverse ranking to cover these alternatives. Existing search diversification algorithms generate diverse document rankings using explicit or implicit methods based on topical similarity. The goal of this paper is to evaluate the impact of applying these existing document diversification frameworks to the problem of answer diversification to determine if topical diversity is related to answer diversity. Using two common diversification algorithms, xQUAD and PM-2, and three question answering test collections, we show that topic diversification can help to generate more effective rankings but is not consistent across different queries and test collections.