Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework

Anna Wegmann, Dong Nguyen


Abstract
Style is an integral part of natural language. However, evaluation methods for style measures are rare, often task-specific and usually do not control for content. We propose the modular, fine-grained and content-controlled similarity-based STyle EvaLuation framework (STEL) to test the performance of any model that can compare two sentences on style. We illustrate STEL with two general dimensions of style (formal/informal and simple/complex) as well as two specific characteristics of style (contrac’tion and numb3r substitution). We find that BERT-based methods outperform simple versions of commonly used style measures like 3-grams, punctuation frequency and LIWC-based approaches. We invite the addition of further tasks and task instances to STEL and hope to facilitate the improvement of style-sensitive measures.
Anthology ID:
2021.emnlp-main.569
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7109–7130
Language:
URL:
https://aclanthology.org/2021.emnlp-main.569
DOI:
10.18653/v1/2021.emnlp-main.569
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.569.pdf
Code
 nlpsoc/stel