Grammatical Error Correction (GEC) is the task of automatically detecting and
correcting errors in text. The task not only includes the correction of
grammatical errors, such as missing prepositions and mismatched subject-verb
agreement, but also orthographic and semantic errors, such as misspellings and
word choice errors respectively. The field has seen significant progress in the
last decade, motivated in part by a series of five shared tasks, which drove
the development of rule-based methods, statistical classifiers, statistical
machine translation, and finally neural machine translation systems which
represent the current dominant state of the art. In this survey paper, we
condense the field into a single article and first outline some of the
linguistic challenges of the task, introduce the most popular datasets that are
available to researchers (for both English and other languages), and summarise
the various methods and techniques that have been developed with a particular
focus on artificial error generation. We next describe the many different
approaches to evaluation as well as concerns surrounding metric reliability,
especially in relation to subjective human judgements, before concluding with
an overview of recent progress and suggestions for future work and remaining
challenges. We hope that this survey will serve as comprehensive resource for
researchers who are new to the field or who want to be kept apprised of recent
developments.

在此综述文章中，我们介绍了在自然语言处理领域中的语法错误纠正任务，其中包括越来越流行的神经机器翻译系统，同时概述了任务中的语言学挑战以及常用的数据集和评价方法。我们还介绍了人类主观判断相关的可靠度指标，并总结了近年来的发展以及未来工作和仍面临的挑战，希望本综述能为新手或关注近期发展的研究人员提供全面的资源。

语法错误纠正：现有技术综述

Grammatical Error Correction: A Survey of the State of the Art

While coreference resolution typically involves various linguistic
challenges, recent models are based on a single pairwise scorer for all types
of pairs. We present LingMess, a new coreference model that defines different
categories of coreference cases and optimize multiple pairwise scorers, where
each scorer learns a specific set of linguistic challenges. Our model
substantially improves pairwise scores for most categories and outperforms
cluster-level performance on Ontonotes and 5 additional datasets. Our model is
available in this https URL

本研究提出 LingMess 作为一种新型的指代消解模型，其将不同类型的指代消解案例定义为不同的类别，并针对每个类别优化多个配对评分器，取得了 OntoNotes 和其他 5 个数据集上的更好结果，从而改进了大多数类别的配对得分，并优于聚类级别的性能表现。

LingMess: 基于语言信息的多专家评分器用于指代消解

LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution

We present an expanded version of our previously released Kazakh
text-to-speech (KazakhTTS) synthesis corpus. In the new KazakhTTS2 corpus, the
overall size has increased from 93 hours to 271 hours, the number of speakers
has risen from two to five (three females and two males), and the topic
coverage has been diversified with the help of new sources, including a book
and Wikipedia articles. This corpus is necessary for building high-quality TTS
systems for Kazakh, a Central Asian agglutinative language from the Turkic
family, which presents several linguistic challenges. We describe the corpus
construction process and provide the details of the training and evaluation
procedures for the TTS system. Our experimental results indicate that the
constructed corpus is sufficient to build robust TTS models for real-world
applications, with a subjective mean opinion score ranging from 3.6 to 4.2 for
all the five speakers. We believe that our corpus will facilitate speech and
language research for Kazakh and other Turkic languages, which are widely
considered to be low-resource due to the limited availability of free
linguistic data. The constructed corpus, code, and pretrained models are
publicly available in our GitHub repository.

本研究介绍了 KazakhTTS2 语料库的构建过程，并提供了 TTS 系统的训练和评估细节。本语料库足以为 Kazakh 和其他 Turkic 语言的语音和语言研究提供帮助，其公开可用于构建高质量的 TTS 系统。