DiscoScore: Evaluating text generation with BERT and discourse coherence

Abstract:

Recently, there has been a growing interest in designing text generation systems from a discourse coherence perspective, e.g., modeling the interdependence between sentences. Still, recent BERT-based evaluation metrics are weak in recognizing coherence, and thus are not reliable in a way to spot the discourse-level improvements of those text generation systems. In this work, we introduce DiscoScore, a parametrized discourse metric, which uses BERT to model discourse coherence from different perspectives, driven by Centering theory. Our experiments encompass 16 non-discourse and discourse metrics, including DiscoScore and popular coherence models, evaluated on summarization and document-level machine translation (MT). We find that (i) the majority of BERT-based metrics correlate much worse with human rated coherence than early discourse metrics, invented a decade ago; (ii) the recent state-of-the-art BARTScore is weak when operated at system level—which is particularly problematic as systems are typically compared in this manner. DiscoScore, in contrast, achieves strong system-level correlation with human ratings, not only in coherence but also in factual consistency and other aspects, and surpasses BARTScore by over 10 correlation points on average. Further, aiming to understand DiscoScore, we provide justifications to the importance of discourse coherence for evaluation metrics, and explain the superiority of one variant over another. Our code is available at https://github.com/AIPHES/DiscoScore.

SEEK ID: https://publications.h-its.org/publications/1588

Research Groups: Natural Language Processing

Publication type: InProceedings

Citation: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, Dubrovnik, Croatia, May 2023, pp.3865-3883

Date Published: 2nd May 2023

URL: https://aclanthology.org/2023.eacl-main.278.pdf

Registered Mode: manually

Authors: Wei Zhao, Michael Strube, Steffen Eger

help Submitter
Activity

Views: 1551

Created: 6th Feb 2023 at 14:16

Last updated: 5th Mar 2024 at 21:25

help Tags

This item has not yet been tagged.

help Attributions

None

Powered by
(v.1.14.2)
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH