Aligning Human and Computational Coherence Evaluations

Abstract

Automated coherence metrics constitute an efficient and popular way to evaluate topic models. Previous works present a mixed picture of their presumed correlation with human judgment. This work proposes a novel sampling approach to mine topic representations at a large-scale while seeking to mitigate bias from sampling, enabling the investigation of widely-used automated coherence metrics via large corpora. Additionally, this work proposes a novel user study design, an amalgamation of different proxy tasks, to derive a finer insight into the human decision-making processes. This design subsumes the purpose of simple rating and outlier-detection user studies. Similar to the sampling approach, the user study conducted is equally extensive. Usually, when substantiating the use of these metrics, human responses are treated as the golden standard. This work further investigates the reliability of human judgment by flipping the comparison and conducting a novel extended analysis of human response at the group and individual level against a generic corpus. The investigation results show a moderate to good correlation between these metrics and human judgment, especially for generic corpora, and derive further insights into the human perception of coherence. Analysing inter-metric correlations across corpora shows moderate to good correlation amongst these metrics. As these metrics depend on corpus statistics, this work further investigates the topical differences between corpora revealing nuances in applications of these metrics.