Multi-Perspective Document Revision (2022)

Abstract: This paper presents a novel multi-perspective document revision task. In conventional studies on document revision, tasks such as grammatical error correction, sentence reordering, and discourse relation classification have been performed individually; however, these tasks simultaneously should be revised to improve the readability and clarity of a whole document. Thus, our study defines multi-perspective document revision as a task that simultaneously revises multiple perspectives. To model the task, we design a novel Japanese multi-perspective document revision dataset that simultaneously handles seven perspectives to improve the readability and clarity of a document. Although a large amount of data that simultaneously handles multiple perspectives is needed to model multi-perspective document revision elaborately, it is difficult to prepare such a large amount of this data. Therefore, our study offers a multi-perspective document revision modeling method that can use a limited amount of matched data (i.e., data for the multi-perspective document revision task) and external partially-matched data (e.g., data for the grammatical error correction task). Experiments using our created dataset demonstrate the effectiveness of using multiple partially-matched datasets to model the multi-perspective document revision task.

## introduction with the advance of natural language processing technology using deep learning, applications for writing support systems have been developed @xcite . such systems often implement a grammatical error correction task that corrects errors such as typos and mistakes in inflected verb forms @xcite . it is easy for the reader to understand an error-free document, and the lack of errors can allow for smooth text communication. in addition, it is crucial to revise a document automatically because it is difficult to read one's writing objectively, and it is time-consuming for a third party to revise the document. the document revision task has been studied in the natural language processing field by being broken down into partial tasks. grammatical error correction tasks have been studied actively @xcite @xcite , and modeling with the sequence-to-sequence (seq2seq) model has achieved high performance with the advance of deep learning @xcite @xcite . in addition, tasks considering the relationship between sentences in a document are sentence ordering @xcite and discourse relation classification @xcite . these tasks have achieved high performance using deep learning, as with the grammatical error correction task. however, studies that simultaneously revise multiple perspectives have not been well considered. to advance writing support, it is important not only to correct grammatical errors in a single sentence but also to improve the readability and clarity of a whole document. for example, when we manually perform document revision, we attempt to correct grammatical errors, split a long sentence into shorter sentences, and consider the relationships between sentences, such as by reordering them to obtain a consistent order and by performing conjunction insertion. accordingly, this paper addresses a novel multi-perspective document revision task that simultaneously considers various perspectives such as grammatical error correction, long sentence splitting, sentence reordering, and conjunction insertion, as shown in figure 1 . to address the multi-perspective document revision task, we need to define this task and design a suitable dataset. in this paper, we define ## japanese multi-perspective document revision dataset this section details a new dataset for a japanese multi-perspective document revision task. the dataset contains paired data consisting of source and revised documents in japanese. the source documents were written by japanese crowd workers, and the reference documents were revised by two japanese labelers. to revise documents, we defined seven perspectives that have been individually used in general document revision problems. ## experiments we experimentally evaluated the effectiveness of this modeling method that can use both a matched dataset and multiple partially-matched datasets. ## conclusion in this paper, we proposed a novel multiperspective document revision task that revises multiple perspectives simultaneously to improve the readability and clarity of a document. we created a dataset that simultaneously addresses seven perspectives by using a crowdsourcing service. in addition, to model the multi-perspective document revision task, we presented a seq2seq modeling method with multiple "on-off" switches. this method allowed us to effectively use a multiperspective document revision dataset and partiallymatched datasets, the grammatical error correction and the conjunction insertion datasets. the experimental results obtained using our created dataset demonstrated that using switches is important for modeling the multi-perspective document revision task. in our future work, we will increase the number of partial tasks (e.g., sentence reordering) and develop a model architecture that is suitable for handling much longer documents.