This paper investigates the problem of Named Entity Recognition (NER) for extreme low-resource languages with only a few hundred tagged data samples. NER is a fundamental task in Natural Language Processing (NLP). A critical driver accelerating NER systems' progress is the existence of large-scale language corpora that enable NER systems to achieve outstanding performance in languages such as English and French with abundant training data. However, NER for low-resource languages remains relatively unexplored. In this paper, we introduce Mask Augmented Named Entity Recognition (MANER), a new methodology that leverages the distributional hypothesis of pre-trained masked language models (MLMs) for NER. The <mask> token in pre-trained MLMs encodes valuable semantic contextual information. MANER re-purposes the <mask> token for NER prediction. Specifically, we prepend the <mask> token to every word in a sentence for which we would like to predict the named entity tag. During training, we jointly fine-tune the MLM and a new NER prediction head attached to each <mask> token. We demonstrate that MANER is well-suited for NER in low-resource languages; our experiments show that for 100 languages with as few as 100 training examples, it improves on state-of-the-art methods by up to 48% and by 12% on average on F1 score. We also perform detailed analyses and ablation studies to understand the scenarios that are best-suited to MANER.

Pre-trained Language Models (PLMs) have been applied in NLP tasks and achieve promising results. Nevertheless, the fine-tuning procedure needs labeled data of the target domain, making it difficult to learn in low-resource and non-trivial labeled scenarios. To address these challenges, we propose Prompt-based Text Entailment (PTE) for low-resource named entity recognition, which better leverages knowledge in the PLMs. We first reformulate named entity recognition as the text entailment task. The original sentence with entity type-specific prompts is fed into PLMs to get entailment scores for each candidate. The entity type with the top score is then selected as final label. Then, we inject tagging labels into prompts and treat words as basic units instead of n-gram spans to reduce time complexity in generating candidates by n-grams enumeration. Experimental results demonstrate that the proposed method PTE achieves competitive performance on the CoNLL03 dataset, and better than fine-tuned counterparts on the MIT Movie and Few-NERD dataset in low-resource settings.

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.

我们利用预训练的语言模型来解决两种低资源语言的复杂NER任务：中文和西班牙语。我们使用整个单词掩码（WWM）的技术来提高大型和无监督的语料库的掩盖语言建模目标。我们在微调的BERT层之上进行多个神经网络体系结构，将CRF，Bilstms和线性分类器结合在一起。我们所有的模型都优于基线，而我们的最佳性能模型在盲目测试集的评估排行榜上获得了竞争地位。

通常，在自然语言处理领域，识别指定实体是一项实用且具有挑战性的任务。由于混合的性质导致语言复杂性，因此在代码混合文本上命名的实体识别是进一步的挑战。本文介绍了CMNERONE团队在Semeval 2022共享任务11 Multiconer的提交。代码混合的NER任务旨在识别代码混合数据集中的命名实体。我们的工作包括在代码混合数据集上的命名实体识别（NER），来利用多语言数据。我们的加权平均F1得分为0.7044，即比基线大6％。

几乎没有命名的实体识别（NER）对于在有限的资源领域中标记的实体标记至关重要，因此近年来受到了适当的关注。现有的几声方法主要在域内设置下进行评估。相比之下，对于这些固有的忠实模型如何使用一些标记的域内示例在跨域NER中执行的方式知之甚少。本文提出了一种两步以理性为中心的数据增强方法，以提高模型的泛化能力。几个数据集中的结果表明，与先前的最新方法相比，我们的模型无形方法可显着提高跨域NER任务的性能，包括反事实数据增强和及时调用方法。我们的代码可在\ url {https://github.com/lifan-yuan/factmix}上获得。

来自变压器（BERT）的双向编码器表示显示了各种NLP任务的奇妙改进，并且已经提出了其连续的变体来进一步提高预先训练的语言模型的性能。在本文中，我们的目标是首先介绍中国伯特的全文掩蔽（WWM）策略，以及一系列中国预培训的语言模型。然后我们还提出了一种简单但有效的型号，称为Macbert，这在几种方面提高了罗伯塔。特别是，我们提出了一种称为MLM作为校正（MAC）的新掩蔽策略。为了展示这些模型的有效性，我们创建了一系列中国预先培训的语言模型，作为我们的基线，包括BERT，Roberta，Electra，RBT等。我们对十个中国NLP任务进行了广泛的实验，以评估创建的中国人托管语言模型以及提议的麦克白。实验结果表明，Macbert可以在许多NLP任务上实现最先进的表演，我们还通过几种可能有助于未来的研究的调查结果来消融细节。我们开源我们的预先培训的语言模型，以进一步促进我们的研究界。资源可用：https://github.com/ymcui/chinese-bert-wwm

In recent years, multilingual pre-trained language models have gained prominence due to their remarkable performance on numerous downstream Natural Language Processing tasks (NLP). However, pre-training these large multilingual language models requires a lot of training data, which is not available for African Languages. Active learning is a semi-supervised learning algorithm, in which a model consistently and dynamically learns to identify the most beneficial samples to train itself on, in order to achieve better optimization and performance on downstream tasks. Furthermore, active learning effectively and practically addresses real-world data scarcity. Despite all its benefits, active learning, in the context of NLP and especially multilingual language models pretraining, has received little consideration. In this paper, we present AfroLM, a multilingual language model pretrained from scratch on 23 African languages (the largest effort to date) using our novel self-active learning framework. Pretrained on a dataset significantly (14x) smaller than existing baselines, AfroLM outperforms many multilingual pretrained language models (AfriBERTa, XLMR-base, mBERT) on various NLP downstream tasks (NER, text classification, and sentiment analysis). Additional out-of-domain sentiment analysis experiments show that \textbf{AfroLM} is able to generalize well across various domains. We release the code source, and our datasets used in our framework at https://github.com/bonaventuredossou/MLM_AL.

对于自然语言处理中的许多任务，将知识从一个域转移到另一个领域至关重要，尤其是当目标域中的可用数据量受到限制时。在这项工作中，我们在指定实体识别（NER）的背景下提出了一种新颖的域适应方法。我们提出了一种两步方法，该方法由可变基本模块和模板模块组成，该模块在简单的描述模式的帮助下利用了预训练的语言模型中捕获的知识。我们的方法简单而通用，可以在几次射击和零拍设置中应用。评估我们在许多不同数据集中的轻量级方法表明，它可以将最新基准的性能提高2-5％的F1分数。

多语言语言模型（\ mllms），如mbert，xlm，xlm-r，\ textit {etc。}已成为一种可行的选择，使预先估计到大量语言的力量。鉴于他们的成功在零射击转移学习中，在（i）建立更大的\ mllms〜覆盖了大量语言（ii）创建覆盖更广泛的任务和语言来评估的详尽工作基准mllms〜（iii）分析单音零点，零拍摄交叉和双语任务（iv）对Monolingual的性能，了解\ mllms〜（v）增强（通常）学习的通用语言模式（如果有的话）有限的容量\ mllms〜以提高他们在已见甚至看不见语言的表现。在这项调查中，我们审查了现有的文学，涵盖了上述与\ MLLMS有关的广泛研究领域。根据我们的调查，我们建议您有一些未来的研究方向。

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models (Peters et al., 2018a;Radford et al., 2018), BERT is designed to pretrain deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers. As a result, the pre-trained BERT model can be finetuned with just one additional output layer to create state-of-the-art models for a wide range of tasks, such as question answering and language inference, without substantial taskspecific architecture modifications.BERT is conceptually simple and empirically powerful. It obtains new state-of-the-art results on eleven natural language processing tasks, including pushing the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement).

Pre-trained models have achieved remarkable success in natural language processing (NLP). However, existing pre-training methods underutilize the benefits of language understanding for generation. Inspired by the idea of Generative Adversarial Networks (GANs), we propose a GAN-style model for encoder-decoder pre-training by introducing an auxiliary discriminator, unifying the ability of language understanding and generation in a single model. Our model, named as GanLM, is trained with two pre-training objectives: replaced token detection and replaced token denoising. Specifically, given masked source sentences, the generator outputs the target distribution and the discriminator predicts whether the target sampled tokens from distribution are incorrect. The target sentence is replaced with misclassified tokens to construct noisy previous context, which is used to generate the gold sentence. In general, both tasks improve the ability of language understanding and generation by selectively using the denoising data. Extensive experiments in language generation benchmarks show that GanLM with the powerful language understanding capability outperforms various strong pre-trained language models (PLMs) and achieves state-of-the-art performance.

This paper presents the work of restoring punctuation for ASR transcripts generated by multilingual ASR systems. The focus languages are English, Mandarin, and Malay which are three of the most popular languages in Singapore. To the best of our knowledge, this is the first system that can tackle punctuation restoration for these three languages simultaneously. Traditional approaches usually treat the task as a sequential labeling task, however, this work adopts a slot-filling approach that predicts the presence and type of punctuation marks at each word boundary. The approach is similar to the Masked-Language Model approach employed during the pre-training stages of BERT, but instead of predicting the masked word, our model predicts masked punctuation. Additionally, we find that using Jieba1 instead of only using the built-in SentencePiece tokenizer of XLM-R can significantly improve the performance of punctuating Mandarin transcripts. Experimental results on English and Mandarin IWSLT2022 datasets and Malay News show that the proposed approach achieved state-of-the-art results for Mandarin with 73.8% F1-score while maintaining a reasonable F1-score for English and Malay, i.e. 74.7% and 78% respectively. Our source code that allows reproducing the results and building a simple web-based application for demonstration purposes is available on Github.

这项研究提供了对僧伽罗文本分类的预训练语言模型的性能的首次全面分析。我们测试了一组不同的Sinhala文本分类任务，我们的分析表明，在包括Sinhala（XLM-R，Labse和Laser）的预训练的多语言模型中，XLM-R是迄今为止Sinhala文本的最佳模型分类。我们还预先培训了两种基于罗伯塔的单语僧伽罗模型，它们远远优于僧伽罗的现有预训练的语言模型。我们表明，在微调时，这些预训练的语言模型为僧伽罗文本分类树立了非常强大的基线，并且在标记数据不足以进行微调的情况下非常强大。我们进一步提供了一组建议，用于使用预训练的模型进行Sinhala文本分类。我们还介绍了新的注释数据集，可用于僧伽罗文本分类的未来研究，并公开发布我们的预培训模型。

自然语言处理领域（NLP）最近看到使用预先接受训练的语言模型来解决几乎任何任务的大量变化。尽管对各种任务的基准数据集显示了很大的改进，但这些模型通常在非标准域中对临床领域的临床域进行次优，其中观察到预训练文件和目标文件之间的巨大差距。在本文中，我们的目标是通过对语言模型的域特定培训结束这种差距，我们调查其对多种下游任务和设置的影响。我们介绍了预先训练的Clin-X（临床XLM-R）语言模型，并展示了Clin-X如何通过两种语言的十个临床概念提取任务的大幅度优于其他预先训练的变压器模型。此外，我们展示了如何通过基于随机分裂和交叉句子上下文的集合来利用我们所提出的任务和语言 - 无人机模型架构进一步改善变压器模型。我们在低资源和转移设置中的研究显​​示，尽管只有250个标记的句子，但在只有250个标记的句子时，缺乏带注释数据的稳定模型表现。我们的结果突出了专业语言模型作为非标准域中的概念提取的Clin-X的重要性，但也表明我们的任务 - 无人机模型架构跨越测试任务和语言是强大的，以便域名或任务特定的适应不需要。 Clin-Xlanguage模型和用于微调和传输模型的源代码在https://github.com/boschresearch/clin\_x/和Huggingface模型集线器上公开使用。

Pre-trained language models are trained on large-scale unsupervised data, and they can be fine-tuned on small-scale labeled datasets and achieve good results. Multilingual pre-trained language models can be trained on multiple languages and understand multiple languages at the same time. At present, the research on pre-trained models mainly focuses on rich-resource language, while there is relatively little research on low-resource languages such as minority languages, and the public multilingual pre-trained language model can not work well for minority languages. Therefore, this paper constructs a multilingual pre-trained language model named MiLMo that performs better on minority language tasks, including Mongolian, Tibetan, Uyghur, Kazakh and Korean. To solve the problem of scarcity of datasets on minority languages and verify the effectiveness of the MiLMo model, this paper constructs a minority multilingual text classification dataset named MiTC, and trains a word2vec model for each language. By comparing the word2vec model and the pre-trained model in the text classification task, this paper provides an optimal scheme for the downstream task research of minority languages. The final experimental results show that the performance of the pre-trained model is better than that of the word2vec model, and it has achieved the best results in minority multilingual text classification. The multilingual pre-trained language model MiLMo, multilingual word2vec model and multilingual text classification dataset MiTC are published on https://milmo.cmli-nlp.com.

最近，大型预用语言模型（LMS）越来越受欢迎。培训这些模型需要更多的计算资源，并且大多数现有模型仅在英文文本上培训。以其他语言训练这些模型非常昂贵。为了缓解这个问题，我们介绍了一种叫做威施塞的方法 - 将英语模型传输到新语言。我们将英语模型的销量与目标语言中的销量交换，并初始化令牌嵌入式，以便通过利用覆盖英语和目标语言的多语言静态字嵌入来初始化令牌嵌入式。我们使用Wechsel将GPT-2和Roberta模型转移到4种其他语言（法语，德语，中文和斯瓦希里语）。 Wechsel通过以前提出的跨语言参数转移和优于比较大小的模型来改善从目标语言的划痕训练的相当大小的型号，距离培训速度较小。我们的方法使培训大型语言模型为新语言更容易访问，更少损害环境。我们宣传我们的代码和型号。

我们介绍ASNER，这是一种使用基线阿萨姆语NER模型的低资源阿萨姆语言的命名实体注释数据集。该数据集包含大约99k代币，其中包括印度总理和阿萨姆人戏剧演讲中的文字。它还包含个人名称，位置名称和地址。拟议的NER数据集可能是基于深神经的阿萨姆语言处理的重要资源。我们通过训练NER模型进行基准测试数据集并使用最先进的体系结构评估被监督的命名实体识别（NER），例如FastText，Bert，XLM-R，Flair，Muril等。我们实施了几种基线方法，标记BI-LSTM-CRF体系结构的序列。当使用Muril用作单词嵌入方法时，所有基线中最高的F1得分的准确性为80.69％。带注释的数据集和最高性能模型公开可用。

NLP是与计算机或机器理解和解释人类语言的能力有关的人工智能和机器学习的一种形式。语言模型在文本分析和NLP中至关重要，因为它们允许计算机解释定性输入并将其转换为可以在其他任务中使用的定量数据。从本质上讲，在转移学习的背景下，语言模型通常在大型通用语料库上进行培训，称为预训练阶段，然后对特定的基本任务进行微调。结果，预训练的语言模型主要用作基线模型，该模型包含了对上下文的广泛掌握，并且可以进一步定制以在新的NLP任务中使用。大多数预训练的模型都经过来自Twitter，Newswire，Wikipedia和Web等通用领域的Corpora培训。在一般文本中训练的现成的NLP模型可能在专业领域效率低下且不准确。在本文中，我们提出了一个名为Securebert的网络安全语言模型，该模型能够捕获网络安全域中的文本含义，因此可以进一步用于自动化，用于许多重要的网络安全任务，否则这些任务将依靠人类的专业知识和繁琐的手动努力。 Securebert受到了我们从网络安全和一般计算域的各种来源收集和预处理的大量网络安全文本培训。使用我们提出的令牌化和模型权重调整的方法，Securebert不仅能够保留对一般英语的理解，因为大多数预训练的语言模型都可以做到，而且在应用于具有网络安全含义的文本时也有效。

多语言预训练的语言模型在跨语言任务上表现出了令人印象深刻的表现。它极大地促进了自然语言处理在低资源语言上的应用。但是，当前的多语言模型仍然有些语言表现不佳。在本文中，我们提出了Cino（中国少数族裔训练的语言模型），这是一种用于中国少数语言的多语言预训练的语言模型。它涵盖了标准的中文，Yue中文和其他六种少数民族语言。为了评估多语言模型在少数族裔语言上的跨语性能力，我们从Wikipedia和新闻网站收集文档，并构建两个文本分类数据集，WCM（Wiki-Chinese-Minority）和CMNEWS（中国最少的新闻）。我们表明，Cino在各种分类任务上的表现明显优于基准。Cino模型和数据集可在http://cino.hfl-rc.com上公开获得。