In the era of digital healthcare, the huge volumes of textual information generated every day in hospitals constitute an essential but underused asset that could be exploited with task-specific, fine-tuned biomedical language representation models, improving patient care and management. For such specialized domains, previous research has shown that fine-tuning models stemming from broad-coverage checkpoints can largely benefit additional training rounds over large-scale in-domain resources. However, these resources are often unreachable for less-resourced languages like Italian, preventing local medical institutions to employ in-domain adaptation. In order to reduce this gap, our work investigates two accessible approaches to derive biomedical language models in languages other than English, taking Italian as a concrete use-case: one based on neural machine translation of English resources, favoring quantity over quality; the other based on a high-grade, narrow-scoped corpus natively written in Italian, thus preferring quality over quantity. Our study shows that data quantity is a harder constraint than data quality for biomedical adaptation, but the concatenation of high-quality data can improve model performance even when dealing with relatively size-limited corpora. The models published from our investigations have the potential to unlock important research opportunities for Italian hospitals and academia. Finally, the set of lessons learned from the study constitutes valuable insights towards a solution to build biomedical language models that are generalizable to other less-resourced languages and different domain settings.

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.

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。

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。

预先训练的上下文化文本表示模型学习自然语言的有效表示，以使IT机器可以理解。在注意机制的突破之后，已经提出了新一代预磨模的模型，以便自变压器引入以来实现了良好的性能。来自变压器（BERT）的双向编码器表示已成为语言理解的最先进的模型。尽管取得了成功，但大多数可用的型号已经在印度欧洲语言中培训，但是对代表性的语言和方言的类似研究仍然稀疏。在本文中，我们调查了培训基于单语言变换器的语言模型的可行性，以获得代表语言的特定重点是突尼斯方言。我们评估了我们的语言模型对情感分析任务，方言识别任务和阅读理解问答任务。我们表明使用嘈杂的Web爬网数据而不是结构化数据（维基百科，文章等）更方便这些非标准化语言。此外，结果表明，相对小的Web爬网数据集导致与使用较大数据集获得的那些表现相同的性能。最后，我们在所有三个下游任务中达到或改善了最先进的Tunbert模型。我们释放出Tunbert净化模型和用于微调的数据集。

Many prior language modeling efforts have shown that pre-training on an in-domain corpus can significantly improve performance on downstream domain-specific NLP tasks. However, the difficulties associated with collecting enough in-domain data might discourage researchers from approaching this pre-training task. In this paper, we conducted a series of experiments by pre-training Bidirectional Encoder Representations from Transformers (BERT) with different sizes of biomedical corpora. The results demonstrate that pre-training on a relatively small amount of in-domain data (4GB) with limited training steps, can lead to better performance on downstream domain-specific NLP tasks compared with fine-tuning models pre-trained on general corpora.

Motivation: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts.

自然语言处理（NLP）是一个人工智能领域，它应用信息技术来处理人类语言，在一定程度上理解并在各种应用中使用它。在过去的几年中，该领域已经迅速发展，现在采用了深层神经网络的现代变体来从大型文本语料库中提取相关模式。这项工作的主要目的是调查NLP在药理学领域的最新使用。正如我们的工作所表明的那样，NLP是药理学高度相关的信息提取和处理方法。它已被广泛使用，从智能搜索到成千上万的医疗文件到在社交媒体中找到对抗性药物相互作用的痕迹。我们将覆盖范围分为五个类别，以调查现代NLP方法论，常见的任务，相关的文本数据，知识库和有用的编程库。我们将这五个类别分为适当的子类别，描述其主要属性和想法，并以表格形式进行总结。最终的调查介绍了该领域的全面概述，对从业者和感兴趣的观察者有用。

The field of cybersecurity is evolving fast. Experts need to be informed about past, current and - in the best case - upcoming threats, because attacks are becoming more advanced, targets bigger and systems more complex. As this cannot be addressed manually, cybersecurity experts need to rely on machine learning techniques. In the texutual domain, pre-trained language models like BERT have shown to be helpful, by providing a good baseline for further fine-tuning. However, due to the domain-knowledge and many technical terms in cybersecurity general language models might miss the gist of textual information, hence doing more harm than good. For this reason, we create a high-quality dataset and present a language model specifically tailored to the cybersecurity domain, which can serve as a basic building block for cybersecurity systems that deal with natural language. The model is compared with other models based on 15 different domain-dependent extrinsic and intrinsic tasks as well as general tasks from the SuperGLUE benchmark. On the one hand, the results of the intrinsic tasks show that our model improves the internal representation space of words compared to the other models. On the other hand, the extrinsic, domain-dependent tasks, consisting of sequence tagging and classification, show that the model is best in specific application scenarios, in contrast to the others. Furthermore, we show that our approach against catastrophic forgetting works, as the model is able to retrieve the previously trained domain-independent knowledge. The used dataset and trained model are made publicly available

在生物医学语料库中预先培训的语言模型，例如Biobert，最近在下游生物医学任务上显示出令人鼓舞的结果。另一方面，由于嵌入尺寸，隐藏尺寸和层数等因素，许多现有的预训练模型在资源密集型和计算上都是沉重的。自然语言处理（NLP）社区已经制定了许多策略来压缩这些模型，利用修剪，定量和知识蒸馏等技术，从而导致模型更快，更小，随后更易于使用。同样，在本文中，我们介绍了六种轻型模型，即Biodistilbert，Biotinybert，BioMobilebert，Distilbiobert，Tinybiobert和Cmpactactbiobert，并通过掩护的语言在PubMed DataSet上通过掩护数据进行了知识蒸馏而获得的知识蒸馏来获得。建模（MLM）目标。我们在三个生物医学任务上评估了所有模型，并将它们与Biobert-V1.1进行比较，以创建有效的轻量级模型，以与较大的对应物相同。所有模型将在我们的HuggingFace配置文件上公开可用，网址为https://huggingface.co/nlpie，用于运行实验的代码将在https://github.com/nlpie-research/compact-compact-biomedical-transformers上获得。

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model - GatorTron - using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og.

动机：生物医学研究人员和临床从业者的常年挑战是随着出版物和医疗票据的快速增长而待的。自然语言处理（NLP）已成为驯服信息超载的有希望的方向。特别是，大型神经语言模型通过预先绘制的文本预测，通过各种NLP应用中的BERT模型的成功示例，便于通过预先绘制的预先来进行学习。然而，用于结束任务的微调此类模型仍然具有挑战性，特别是具有小标记数据集，这些数据集是生物医学NLP的常见。结果：我们对生物医学NLP的微调稳定性进行了系统研究。我们表明FineTuning性能可能对预先预订的设置敏感，尤其是在低资源域中。大型型号有可能获得更好的性能，但越来越多的模型大小也加剧了FineTuning不稳定性。因此，我们对解决微调不稳定的技术进行了全面的探索。我们表明，这些技术可以大大提高低源生物医学NLP应用的微调性能。具体地，冻结下层有助于标准伯特基型号，而完整的衰减对于BERT-LARD和Electra型号更有效。对于低资源文本相似性任务，如生物，重新初始化顶层是最佳策略。总体而言，占星型词汇和预制促进更强大的微调模型。基于这些调查结果，我们在广泛的生物医学NLP应用方面建立了新的技术。可用性和实施​​：为了促进生物医学NLP的进展，我们释放了我们最先进的预订和微调模型：https://aka.ms/blurb。

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.

深语模型在NLP域中取得了显着的成功。培养深层语言模型的标准方法是从大型未标记的语料库中雇用无监督的学习。但是，这种大型公司仅适用于广泛采用和高资源语言和域名。本研究提出了第一款深语型号DPRK-BERT为朝鲜语言。我们通过编制朝鲜语言的第一个未标记的语料库和微调预先存在的ROK语言模型来实现这一目标。我们将所提出的模型与现有方法进行比较，并显示两个DPRK数据集的显着改进。我们还提供了这种模型的交叉语言版本，其在两种韩语语言中产生了更好的泛化。最后，我们提供与朝鲜语言相关的各种NLP工具，这些工具将培养未来的研究。

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).

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。

获得具有语义注释的文本数据集是一个艰苦的过程，但对于自然语言过程（NLP）的监督培训至关重要。通常，在特定于域的上下文中开发和应用新的NLP管道通常需要定制设计的数据集来以监督机器学习方式解决NLP任务。当使用非英语语言进行医学数据处理时，这会暴露出几个次要和主要的相互联系的问题，例如缺乏任务匹配数据集以及特定于任务的预训练模型。在我们的工作中，我们建议利用审计的语言模型来培训数据获取，以便检索足够大的数据集，以训练更小，更有效的模型，以便使用特定的特定任务。为了证明您的方法的有效性，我们创建了一个自定义数据集，我们用来培训用于德国文本的医学模型，但在原则上我们的方法仍然不依赖语言。我们获得的数据集以及我们的预培训模型可在以下网址公开获取：https：//github.com/frankkramer-lab/gptnermed

我们提出了一个针对德国医学自然语言处理的统计模型，该模型训练了命名实体识别（NER），作为开放的公开模型。这项工作是我们第一个Gernerm模型的精致继任者，我们的工作大大优于我们的工作。我们证明了结合多种技术的有效性，以通过在预审预测的深度语言模型（LM），单词平衡和神经机器翻译上转移学习的方式来实现实体识别绩效。由于开放的公共医疗实体识别模型在德国文本上的稀疏情况，这项工作为医疗NLP作为基线模型的德国研究社区提供了好处。由于我们的模型基于公共英语数据，因此提供了其权重，而无需法律限制使用和分发。示例代码和统计模型可在以下网址获得：https：//github.com/frankkramer-lab/gernermed-pp

对于许多任务，基于变压器的体系结构已经实现了最新的结果，从而导致实践从使用特定于任务的架构到预先训练的语言模型的微调。持续的趋势包括具有越来越多的数据和参数的培训模型，这需要大量资源。它导致了强有力的搜索，以提高基于仅针对英语评估的算法和硬件改进的算法和硬件改进。这引发了有关其可用性的疑问，当应用于小规模的学习问题时，对于资源不足的语言任务，有限的培训数据可用。缺乏适当尺寸的语料库是应用数据驱动和转移学习的方法的障碍。在本文中，我们建立了致力于基于变压器模型的可用性的最新努力，并建议评估这些改进的法语表现，而法语的效果很少。我们通过通过数据增强，超参数优化和跨语性转移来调查各种培训策略来解决与数据稀缺有关的不稳定。我们还为法国弗拉伯特（Fralbert）引入了一种新的紧凑型模型，该模型在低资源环境中被证明具有竞争力。

在法律文本中预先培训的基于变压器的预训练语言模型（PLM）的出现，法律领域中的自然语言处理受益匪浅。有经过欧洲和美国法律文本的PLM，最著名的是Legalbert。但是，随着印度法律文件的NLP申请量的迅速增加以及印度法律文本的区别特征，也有必要在印度法律文本上预先培训LMS。在这项工作中，我们在大量的印度法律文件中介绍了基于变压器的PLM。我们还将这些PLM应用于印度法律文件的几个基准法律NLP任务，即从事实，法院判决的语义细分和法院判决预测中的法律法规识别。我们的实验证明了这项工作中开发的印度特定PLM的实用性。