Read, Revise, Repeat: A System Demonstration for Human-in-the-loop Iterative Text Revision (2022)

Abstract: Revision is an essential part of the human writing process. It tends to be strategic, adaptive, and, more importantly, iterative in nature. Despite the success of large language models on text revision tasks, they are limited to non-iterative, one-shot revisions. Examining and evaluating the capability of large language models for making continuous revisions and collaborating with human writers is a critical step towards building effective writing assistants. In this work, we present a human-in-the-loop iterative text revision system, Read, Revise, Repeat (R3), which aims at achieving high quality text revisions with minimal human efforts by reading model-generated revisions and user feedbacks, revising documents, and repeating human-machine interactions. In R3, a text revision model provides text editing suggestions for human writers, who can accept or reject the suggested edits. The accepted edits are then incorporated into the model for the next iteration of document revision. Writers can therefore revise documents iteratively by interacting with the system and simply accepting/rejecting its suggested edits until the text revision model stops making further revisions or reaches a predefined maximum number of revisions. Empirical experiments show that R3 can generate revisions with comparable acceptance rate to human writers at early revision depths, and the human-machine interaction can get higher quality revisions with fewer iterations and edits. The collected human-model interaction dataset and system code are available at https://github.com/vipulraheja/IteraTeR . Our system demonstration is available at https://youtu.be/lK08tIpEoaE .

## introduction text revision is a crucial part of writing. specifically, text revision involves identifying discrepan- computational modeling of the iterative text revision process is essential for building intelligent and interactive writing assistants. most prior works on the development of neural text revision systems @xcite @xcite do not take the iterative nature of text revision and human feedback on suggested revisions into consideration. the direct application of such revision systems in an iterative way, however, could generate some "noisy" edits and require much burden on human writers to fix the noise. therefore, we propose to collect human feedback at each iteration of revision to filter out those harmful noisy edits and produce revised documents of higher quality. in this work, we present a novel human-in-theloop iterative text revision system, read, revise, repeat (r3), which reads model-generated revisions and user feedbacks, revises documents, and repeats human-machine interactions in an iterative way, as depicted in figure 1 . first, users write a document as input to the system or choose one from a candidate document set to edit. then, the text revision system provides multiple editing suggestions with their edits and intents. users can accept or reject the editing suggestions in an iterative way and stop revision when no editing suggestions are provided or the model reaches the maximum revision limit. the overall model performance can be estimated by calculating the acceptance rate throughout all editing suggestions. r3 provides numerous benefits over existing writing assistants for text revision. first, r3 improves the overall writing experience for writers by making it more interpretable, controllable, and productive: on the one hand, writers don't have to (re-)read the parts of the text that are already high quality, and this, in turn, helps them focus on larger writing goals ( §4.2); on the other hand, by showing edit intentions for every suggested edit, which users can further decide to accept or reject, r3 provides them with more fine-grained control over the text revision process compared to other one-shot based text revision systems @xcite , and are limited in both interpretability and controllability. second, r3 improves the revision efficiency. the human-machine interaction can help the system produce higher quality revisions with fewer iterations and edits, and the empirical experiments in §4.2 validate this claim. to the best of our knowledge, r3 is the first text revision system in literature that can perform iterative text revision in collaboration by human writers and revision models. in this paper, we make three major contributions: ## related work previous works on modeling text revision @xcite @xcite have ignored the iterative nature of the task, and simplified it into a one-shot "original-to-final" sentence-to-sentence generation task. however, in practice, at every revision step, multiple edits happen at the document-level which also play an important role in text revision. for instance, reordering and deleting sentences to improve the coherence. more importantly, performing multiple highquality edits at once is very challenging. continuing the previous example, document readability can degrade after reordering sentences, and further adding transitional phrases is often required to make the document more coherent and readable. therefore, one-shot sentence-to-sentence text revision formulation is not sufficient to deal with real-world challenges in text revision tasks. while some prior works on text revision @xcite @xcite have proposed humanmachine collaborative writing interfaces, they are mostly focused on collecting human-machine interaction data for training better neural models, rather than understanding the iterative nature of the text revision process, or the model's ability to adjust editing suggestions according to human feedback. another line of work by @xcite ; @xcite on creative writing designed humanmachine interaction interfaces to encourage new content generation. however, text revision focuses on improving the quality of existing writing and keeping the original content as much as possible. in this work, we provide a human-in-the-loop text revision system to make helpful editing suggestions by interacting with users in an iterative way. ## system overview figure 1 shows the general pipeline of r3 humanin-the-loop iterative text revision system. in this section, we will describe the development details of the text revision models and demonstrate our user interfaces. we first formulate an iterative text revision process: given a source document foot_0 d t-1 , at each revision depth t, a text revision system will apply a set of edits to get the revised document d t . the system will continue iterating revision until the revised document d t satisfies a set of predefined stopping criteria, such as reaching a predefined maximum revision depth t max , or making no edits between d t-1 and d t . ## experiments we conduct experiments to answer the following research questions: rq1 how likely are users to accept the editing suggestions predicted by our text revision system? this question is designed to evaluate whether our text revision system can generate high quality edits. rq2 which types of edit intentions are more likely to be accepted by users? this question is aimed to identify which types of edits are more favored by users. rq3 does user feedback in r3 help produce higher quality of revised documents? this question is proposed to validate the effectiveness of human-in-the-loop component in r3. ## discussion and future directions when r3 generates revisions at deeper depths, we observe a decrease in the acceptance ratio by human users. it is crucial to create a text revision system that can learn different revision strategies at each iteration and generate high quality edits at deeper revision levels. editing suggestions provided by our text revision generation models could be improved. particularly, fluency edits show a huge gap between human and system revisions (45.05% and 82.02%). future work could focus on developing more powerful text revision generation models. in our human-machine interaction, we restrict the users' role to accept or reject the model's predictions. even with minimal human interaction, our experiment shows comparable or even better revision quality as compared to human writers at early revision depths. a potential future direction for human-machine collaborative text revision would be to develop advanced human-machine interaction interfaces, such as asking users to re-write the machine-revised text. also, a larger-scale user study could be carried out to derive more meaningful statistics (e.g. optimal number of revision depths and edit suggestions) and investigate if there is any intriguing user behavior in the iterative revision process. for example, as mentioned in the users' feedback, it would be interesting to check if users behave differently when they are asked to accept/reject edit suggestions provided for their own texts as opposed to the texts written by a third party. ## conclusion in this work, we develop an interactive iterative text revision system r3 that is able to effectively assist users to make revisions and improve the quality of existing documents. r3 can generate higher quality revisions while minimizing the human efforts. users are provided with a reviewing interface to accept or reject system suggesting edits. the user-validated edits are then propagated to the next revision depth to get further improved revisions. empirical results show that r3 can generate iterative text revisions with acceptance rates comparable or even better than human writers at early revision depths. due to its high lethality amongst the elderly, nursing homes are in the eye of the covid-19 storm. emerging new test procedures , such as antigen or rt-lamp tests, might enable us to protect nursing home residents by means of preventive screening strategies. here, we develop a novel agent-based epidemiological model for the spread of sars-cov-2 in nursing homes to identify optimal preventive testing strategies to curb this spread . the model is microscopically. the model is calibrated to high-resolution data from actual nursing homes in austria, including the detailed networks of social contacts of their residents and information on past outbreaks. emerging new test procedures , such as antigen or rt-lamp tests, might enable us to protect nursing home residents by means of preventive screening strategies. here, we develop a novel detailed agent-based epidemiological model for the spread of sars-cov-2 in nursing homes to identify optimal preventive testing strategiesto curb this spread . the model is microscopically calibrated to high-resolution data from actual nursing homes in austria, including the detailed networks of social contacts of their resident detailed social contact networks and information on past outbreaks. due to its high lethality amongst the elderly, n n ursing homes are in the eye of the covid-19 storm. emerging new test procedures might enable us to protect nursing home residents by means of preventive screening strategies . here, we develop a novel agent-based epidemiological model for the spread of sars-cov-2 in nursing homes to identify optimal preventive testing strategies. the model is calibrated to high-resolution data from actual nursing homes in austria, including the detailed networks of social contacts of their residents and information on past outbreaks.