Evaluating Subtitle Segmentation for End-to-end Generation Systems

TL;DR

This paper evaluates subtitle segmentation methods, introduces a new metric called Sigma for assessing segmentation accuracy independently of text quality, and analyzes how different metrics influence system ranking.

Contribution

The paper systematically analyzes existing segmentation metrics and proposes Sigma, a novel metric that isolates segmentation quality from text content in subtitle generation.

Findings

Sigma effectively separates segmentation quality from text quality.

All metrics can reward high-quality outputs, but system rankings vary.

Sigma shows promise but needs further validation against human judgments.

Abstract

Subtitles appear on screen as short pieces of text, segmented based on formal constraints (length) and syntactic/semantic criteria. Subtitle segmentation can be evaluated with sequence segmentation metrics against a human reference. However, standard segmentation metrics cannot be applied when systems generate outputs different than the reference, e.g. with end-to-end subtitling systems. In this paper, we study ways to conduct reference-based evaluations of segmentation accuracy irrespective of the textual content. We first conduct a systematic analysis of existing metrics for evaluating subtitle segmentation. We then introduce $Sigma$, a new Subtitle Segmentation Score derived from an approximate upper-bound of BLEU on segmentation boundaries, which allows us to disentangle the effect of good segmentation from text quality. To compare $Sigma$ with existing metrics, we further propose a boundary projection method from imperfect hypotheses to the true reference. Results show that all metrics are able to reward high quality output but for similar outputs system ranking depends on each metric's sensitivity to error type. Our thorough analyses suggest $Sigma$ is a promising segmentation candidate but its reliability over other segmentation metrics remains to be validated through correlations with human judgements.