Language identification (LID) is a crucial precursor for NLP, especially for mining web data. Problematically, most of the world's 7000+ languages today are not covered by LID technologies. We address this pressing issue for Africa by introducing AfroLID, a neural LID toolkit for $517$ African languages and varieties. AfroLID exploits a multi-domain web dataset manually curated from across 14 language families utilizing five orthographic systems. When evaluated on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare AfroLID to five existing LID tools that each cover a small number of African languages, finding it to outperform them on most languages. We further show the utility of AfroLID in the wild by testing it on the acutely under-served Twitter domain. Finally, we offer a number of controlled case studies and perform a linguistically-motivated error analysis that allow us to both showcase AfroLID's powerful capabilities and limitations.

Code-Switching, a common phenomenon in written text and conversation, has been studied over decades by the natural language processing (NLP) research community. Initially, code-switching is intensively explored by leveraging linguistic theories and, currently, more machine-learning oriented approaches to develop models. We introduce a comprehensive systematic survey on code-switching research in natural language processing to understand the progress of the past decades and conceptualize the challenges and tasks on the code-switching topic. Finally, we summarize the trends and findings and conclude with a discussion for future direction and open questions for further investigation.

本文介绍了对土耳其语可用于的语料库和词汇资源的全面调查。我们审查了广泛的资源，重点关注公开可用的资源。除了提供有关可用语言资源的信息外，我们还提供了一组建议，并确定可用于在土耳其语言学和自然语言处理中进行研究和建筑应用的数据中的差距。

GPT-3等大型自回归语言模型是几秒钟的学习者，可以在没有微调的情况下执行各种语言任务。虽然已知这些模型能够共同代表许多不同的语言，但他们的培训数据由英语主导，可能限制了它们的交叉概括。在这项工作中，我们在覆盖多种语言的平衡语料库上培训多语言自回归语言模型，并在广泛的任务中研究他们几乎没有零点的学习能力。我们最大的模型，具有75亿参数，在20多种代表语言中，在几种代表语言中，在几种代表性语言中，在几种代表性语言中，在多语言型号推理中表现出可比大小的GPT-3（在0次设置和0次拍摄设置中的绝对精度改善+ 7.4％ 4-拍摄设置中的9.4％）和自然语言推理（每次拍摄和4次设置中的每一个+ 5.4％）。在Flores-101机器翻译基准测试中，我们的模型优于GPT-3在182个翻译方向上有32个培训例子，同时超过45个方向的官方监督基线。我们介绍了模型成功和失败的位置的详细分析，特别是它尤其显示在某些任务中实现交叉语境的内容学习，而仍然存在改善表面的鲁棒性和适应没有a的任务的余地自然冻结形式。最后，我们评估我们在仇恨语音检测中以五种语言的仇恨语音检测的模型，并发现它具有与可比大小的GPT-3模型类似的限制。

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Multilingual language models (MLMs) acquire valuable, generalizable linguistic information during pretraining and have advanced the state of the art on task-specific finetuning. So far, only ~ 28 out of ~2,000 African languages are covered in existing language models. We ameliorate this limitation by developing SERENGETI, a set of massively multilingual language model that covers 517 African languages and language varieties. We evaluate our novel models on eight natural language understanding tasks across 20 datasets, comparing to four MLMs that each cover any number of African languages. SERENGETI outperforms other models on 11 datasets across the eights tasks and achieves 82.27 average F-1. We also perform error analysis on our models' performance and show the influence of mutual intelligibility when the models are applied under zero-shot settings. We will publicly release our models for research.

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.

Twitter包含来自现实世界中的大量语言数据。我们检查了Twitter的低资源语言（例如本地印尼语）的用户生成的内容。为了使NLP在印尼语中工作，它必须考虑本地方言，地理环境和区域文化影响印尼语言。本文确定了我们在构建本地印尼NLP数据集时面临的问题。此外，我们正在开发一个用于创建，收集和分类NLP本地印尼数据集的框架。使用Twitter的地理位置工具自动注释。

Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models. In spite of these efforts, no public benchmark of diverse nature currently exists for evaluation of Arabic. This makes it challenging to measure progress for both Arabic and multilingual language models. This challenge is compounded by the fact that any benchmark targeting Arabic needs to take into account the fact that Arabic is not a single language but rather a collection of languages and varieties. In this work, we introduce ORCA, a publicly available benchmark for Arabic language understanding evaluation. ORCA is carefully constructed to cover diverse Arabic varieties and a wide range of challenging Arabic understanding tasks exploiting 60 different datasets across seven NLU task clusters. To measure current progress in Arabic NLU, we use ORCA to offer a comprehensive comparison between 18 multilingual and Arabic language models. We also provide a public leaderboard with a unified single-number evaluation metric (ORCA score) to facilitate future research.

情感分析是NLP中研究最广泛的应用程序之一，但大多数工作都集中在具有大量数据的语言上。我们介绍了尼日利亚的四种口语最广泛的语言（Hausa，Igbo，Nigerian-Pidgin和Yor \'ub \'a）的第一个大规模的人类通知的Twitter情感数据集，该数据集由大约30,000个注释的推文组成（以及每种语言的大约30,000个）（以及14,000尼日利亚猎人），其中包括大量的代码混合推文。我们提出了文本收集，过滤，处理和标记方法，使我们能够为这些低资源语言创建数据集。我们评估了数据集上的预训练模型和转移策略。我们发现特定于语言的模型和语言适应性芬通常表现最好。我们将数据集，训练的模型，情感词典和代码释放到激励措施中，以代表性不足的语言进行情感分析。

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。

The BERT family of neural language models have become highly popular due to their ability to provide sequences of text with rich context-sensitive token encodings which are able to generalise well to many NLP tasks. We introduce gaBERT, a monolingual BERT model for the Irish language. We compare our gaBERT model to multilingual BERT and the monolingual Irish WikiBERT, and we show that gaBERT provides better representations for a downstream parsing task. We also show how different filtering criteria, vocabulary size and the choice of subword tokenisation model affect downstream performance. We compare the results of fine-tuning a gaBERT model with an mBERT model for the task of identifying verbal multiword expressions, and show that the fine-tuned gaBERT model also performs better at this task. We release gaBERT and related code to the community.

意大利的特征是欧洲一种一种独一无二的语言多样性格局，该景观暗中编码了当地知识，文化传统，艺术表达及其演讲者的历史。但是，意大利的30多种语言品种有几代人内消失的风险。语言技术在保存濒危语言方面具有主要作用，但是目前，它在资源不足，主要缺乏标准拼写术的品种中挣扎，主要用于口语环境。在本文中，我们介绍了意大利的语言背景，并讨论了意大利语言品种开发NLP技术面临的挑战。我们提供潜在的方向，并倡导从以机器为中心转向以说话者为中心的NLP的范式转变。最后，我们建议建立一个当地社区，旨在为意大利语言和方言的言语和语言技术负责，参与式发展。

te reo m \ = aori（称为m \ = aori），新西兰的土著语言在语言技术中的资源不足。 m \ = aori扬声器是双语的，其中m \ = aori用英语进行了代码开关。不幸的是，M \ = AORI语言技术，语言检测和M \ = Aori-English对之间的代码转换检测的资源最少。英语和M \ = AORI都使用罗马衍生的拼字法制作基于规则的系统来检测语言和代码转换限制性。大多数M \ = AORI语言检测是由语言专家手动完成的。这项研究构建了66,016,807个单词的Aori英语双语数据库，并带有单词级语言注释。新西兰议会汉萨德辩论报告用于构建数据库。语言标签是使用特定语言规则和专家手册注释分配的。 M \ = AORI和英语的单词具有相同的拼写，但含义不同。这些词不能根据单词级的语言规则将其归类为M \ = AORI或英语。因此，需要手动注释。还报道了报告数据库的各个方面的分析，例如元数据，逐年分析，经常出现的单词，句子长度和n-grams。这里开发的数据库是新西兰Aotearoa的未来语言和语音技术开发的宝贵工具。遵循标签数据库的方法也可以遵循其他低资源的语言对。

本文使用寄存器预测任务进行了39种语言的基于频率语料库相似性的实验。目的是量化（i）不同语料库与同一语言和（ii）单个语音的同质性之间的距离。这两个目标对于衡量基于语料库的语言分析如何从一个数据集推广到另一个数据集都至关重要。问题在于，以前的工作集中在印欧语上，提出了一个问题，即这些措施是否能够在各种语言上提供强大的概括。本文使用寄存器预测任务来评估跨39种语言的竞争措施：他们能够区分代表不同生产环境的语料库？每个实验都将单个语言的三个语料库与所有语言共享的三个数字寄存器进行比较：社交媒体，网页和Wikipedia。结果表明，语料库相似性的衡量标准保留了不同语言家族，写作系统和形态类型的有效性。此外，当对不域外的语料库，应用于低资源语言以及应用于不同的寄存器集时，这些措施仍然坚固。鉴于我们需要在可用于分析的迅速增加的情况下进行概括，因此这些发现很重要。

在本文中，我们分享了我们努力建立能够翻译一千多种语言的实用机器翻译（MT）系统的发现。我们在三个研究领域中描述了结果：（i）通过利用半监督预训练的语言识别和开发数据驱动的过滤技术来构建1500多种语言的清洁，网挖数据集； （ii）通过利用大规模的多语言模型来开发用于服务不足的语言的实用MT模型，该模型训练了有监督的并行数据，以使用100多种高资源语言和单语言数据集，以增加1000多种语言； （iii）研究这些语言的评估指标的局限性，并对我们MT模型的输出进行定性分析，突出显示了这些类型模型的几种频繁误差模式。我们希望我们的工作为旨在为当前研究的语言构建MT系统的从业者提供有用的见解，并突出显示可以补充Data-Sparse设置中大量多语言模型的弱点的研究方向。

We present, Naamapadam, the largest publicly available Named Entity Recognition (NER) dataset for the 11 major Indian languages from two language families. In each language, it contains more than 400k sentences annotated with a total of at least 100k entities from three standard entity categories (Person, Location and Organization) for 9 out of the 11 languages. The training dataset has been automatically created from the Samanantar parallel corpus by projecting automatically tagged entities from an English sentence to the corresponding Indian language sentence. We also create manually annotated testsets for 8 languages containing approximately 1000 sentences per language. We demonstrate the utility of the obtained dataset on existing testsets and the Naamapadam-test data for 8 Indic languages. We also release IndicNER, a multilingual mBERT model fine-tuned on the Naamapadam training set. IndicNER achieves the best F1 on the Naamapadam-test set compared to an mBERT model fine-tuned on existing datasets. IndicNER achieves an F1 score of more than 80 for 7 out of 11 Indic languages. The dataset and models are available under open-source licenses at https://ai4bharat.iitm.ac.in/naamapadam.

社交媒体平台上的滥用内容的增长增加对在线用户的负面影响。对女同性恋，同性恋者，跨性别或双性恋者的恐惧，不喜欢，不适或不疑虑被定义为同性恋/转铁症。同性恋/翻译语音是一种令人反感的语言，可以总结为针对LGBT +人的仇恨语音，近年来越来越受到兴趣。在线同性恋恐惧症/ Transphobobia是一个严重的社会问题，可以使网上平台与LGBT +人有毒和不受欢迎，同时还试图消除平等，多样性和包容性。我们为在线同性恋和转鸟以及专家标记的数据集提供了新的分类分类，这将允许自动识别出具有同种异体/传递内容的数据集。我们受过教育的注释器并以综合的注释规则向他们提供，因为这是一个敏感的问题，我们以前发现未受训练的众包注释者因文化和其他偏见而诊断倡导性的群体。数据集包含15,141个注释的多语言评论。本文介绍了构建数据集，数据的定性分析和注册间协议的过程。此外，我们为数据集创建基线模型。据我们所知，我们的数据集是第一个已创建的数据集。警告：本文含有明确的同性恋，转基因症，刻板印象的明确陈述，这可能对某些读者令人痛苦。

本文为太平洋地区的低资源和资源不足语言提供了语言识别模型，重点是以前无法使用的奥地利语语言。准确的语言标识是开发语言资源的重要组成部分。本文采用的方法结合了29种南方语言和171种非澳洲语言，以创建从八个数据源绘制的评估集。在评估了六种语言识别方法之后，我们发现基于跳过嵌入的分类器的性能明显高于替代方法。然后，我们系统地将模型中的非澳洲语言的数量增加到总共800种语言，以评估增加语言库存是否会导致对澳洲感兴趣的澳洲语言的精确预测。该评估发现，增加非澳洲语言库存造成的准确性只有最小的影响。进一步的实验使这些语言识别模型适应了代码转换检测，从而在所有29种语言中都能达到高精度。