Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

• URL: http://arxiv.org/abs/2205.11484v1
• Date: Mon, 23 May 2022 17:37:20 GMT
• Title: Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond
• Authors: Masato Mita, Keisuke Sakaguchi, Masato Hagiwara, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui
• Abstract summary: We introduce a new document-revision corpus, TETRA, where professional editors revised academic papers sampled from the ACL anthology. We show the uniqueness of TETRA compared with existing document revision corpora and demonstrate that a fine-tuned pre-trained language model can discriminate the quality of documents after revision even when the difference is subtle.
• Score: 46.130399041820716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges. To go beyond sentence-level automated grammatical error correction to NLP-based document-level revision assistant, there are two major obstacles: (1) there are few public corpora with document-level revisions being annotated by professional editors, and (2) it is not feasible to elicit all possible references and evaluate the quality of revision with such references because there are infinite possibilities of revision. This paper tackles these challenges. First, we introduce a new document-revision corpus, TETRA, where professional editors revised academic papers sampled from the ACL anthology which contain few trivial grammatical errors that enable us to focus more on document- and paragraph-level edits such as coherence and consistency. Second, we explore reference-less and interpretable methods for meta-evaluation that can detect quality improvements by document revision. We show the uniqueness of TETRA compared with existing document revision corpora and demonstrate that a fine-tuned pre-trained language model can discriminate the quality of documents after revision even when the difference is subtle. This promising result will encourage the community to further explore automated document revision models and metrics in future.

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