Writing is, by nature, a strategic, adaptive, and more importantly, an iterative process.A crucial part of writing is editing and revising the text.Previous works on text revision have focused on defining edit intention taxonomies within a single domain or developing computational models with a single level of edit granularity, such as sentence-level edits, which differ from human's revision cycles.This work describes ITERATER: the first largescale, multi-domain, edit-intention annotated corpus of iteratively revised text.In particular, ITERATER is collected based on a new framework to comprehensively model the iterative text revisions that generalize to various domains of formal writing, edit intentions, revision depths, and granularities.When we incorporate our annotated edit intentions, both generative and edit-based text revision models significantly improve automatic evaluations. 1 Through our work, we better understand the text revision process, making vital connections between edit intentions and writing quality, enabling the creation of diverse corpora to support computational modeling of iterative text revisions.

Iterative text revision improves text quality by fixing grammatical errors, rephrasing for better readability or contextual appropriateness, or reorganizing sentence structures throughout a document.Most recent research has focused on understanding and classifying different types of edits in the iterative revision process from human-written text instead of building accurate and robust systems for iterative text revision.In this work, we aim to build an end-to-end text revision system that can iteratively generate helpful edits by explicitly detecting editable spans (where-to-edit) with their corresponding edit intents and then instructing a revision model to revise the detected edit spans.Leveraging datasets from other related text editing NLP tasks, combined with the specification of editable spans, leads our system to more accurately model the process of iterative text refinement, as evidenced by empirical results and human evaluations.Our system significantly outperforms previous baselines on our text revision tasks and other standard text revision tasks, including grammatical error correction, text simplification, sentence fusion, and style transfer.Through extensive qualitative and quantitative analysis, we make vital connections between edit intentions and writing quality, and better computational modeling of iterative text revisions.

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem.We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods.However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions.Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a largescale dataset for it.To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval.This framework provides a reference-free approach for dialog evaluation by finding pseudo-references.Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.

Text style transfer involves rewriting the content of a source sentence in a target style.Despite there being a number of style tasks with available data, there has been limited systematic discussion of how text style datasets relate to each other.This understanding, however, is likely to have implications for selecting multiple data sources for model training.While it is prudent to consider inherent stylistic properties when determining these relationships, we also must consider how a style is realized in a particular dataset.In this paper, we conduct several empirical analyses of existing text style datasets.Based on our results, we propose a categorization of stylistic and dataset properties to consider when utilizing or comparing text style datasets.

Wanyu Du, Yangfeng Ji. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.