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Fine-grained, span-level human evaluation has emerged as a reliable androbust method for evaluating text generation tasks such as summarization,simplification, machine translation and news generation, and the derivedannotations have been useful for training automatic metrics and improvinglanguage models. However, existing annotation tools implemented for theseevaluation frameworks lack the adaptability to be extended to different domainsor languages, or modify annotation settings according to user needs; and, theabsence of a unified annotated data format inhibits the research in multi-tasklearning. In this paper, we introduce Thresh, a unified, customizable anddeployable platform for fine-grained evaluation. With a single YAMLconfiguration file, users can build and test an annotation interface for anyframework within minutes -- all in one web browser window. To facilitatecollaboration and sharing, Thresh provides a community hub that hosts acollection of fine-grained frameworks and corresponding annotations made andcollected by the community, covering a wide range of NLP tasks. For deployment,Thresh offers multiple options for any scale of annotation projects from smallmanual inspections to large crowdsourcing ones. Additionally, we introduce aPython library to streamline the entire process from typology design anddeployment to annotation processing. Thresh is publicly accessible athttps://thresh.tools.

Thresh: A Unified, Customizable and Deployable Platform for Fine-Grained Text Evaluation