Paper Info
Title
On the Relation between Quality-Diversity Evaluation and Distribution-Fitting Goal in Text Generation
Abstract
The goal of text generation models is to fit the underlying real probability distribution of text. For performance evaluation, quality and diversity metrics are usually applied. However, it is still not clear to what extend can the quality-diversity evaluation reflect the distribution-fitting goal. In this paper, we try to reveal such relation in a theoretical approach. We prove that under certain conditions, a linear combination of quality and diversity constitutes a divergence metric between the generated distribution and the real distribution. We also show that the commonly used BLEU/Self-BLEU metric pair fails to match any divergence metric, thus propose CR/NRR as a substitute for quality/diversity metric pair.
Year
Venue
DocType
2020
ICML
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Jianing Li1106.97
Yanyan Lan2100563.59
Jiafeng Guo31737102.17
Xueqi Cheng43148247.04