Beyond the Tip of the Iceberg: Assessing Coherence of Text Classifiers

Abstract

As large-scale, pre-trained language models achieve human-level and superhuman accuracy on existing language understanding tasks, statistical bias in benchmark data and probing studies have recently called into question their true capabilities. For a more informative evaluation than accuracy on text classification tasks can offer, we propose evaluating systems through a novel measure of prediction coherence. We apply our framework to two existing language understanding benchmarks with different properties to demonstrate its versatility. Our experimental results show that this evaluation framework, although simple in ideas and implementation, is a quick, effective, and versatile measure to provide insight into the coherence of machines' predictions.

FFCD: A Fast-and-Frugal Coherence Detection Method

“…Entailment is a closely related concept to Next Sentence Prediction and has received research stimulation due to revolutionary advances in natural language inference (NLI) models [22]- [24]. These models were trained to detect entailment as classification task, and have been used to infer entailment as an indication of coherence in diverse systems [19], [25], [26]. Entailment is a stricter concept, where the goal is to predict whether a sentence pair (premise and hypothesis) is contradictory, in agreement, or neutral.…”