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。

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).

Transformer-based models have pushed state of the art in many areas of NLP, but our understanding of what is behind their success is still limited. This paper is the first survey of over 150 studies of the popular BERT model. We review the current state of knowledge about how BERT works, what kind of information it learns and how it is represented, common modifications to its training objectives and architecture, the overparameterization issue and approaches to compression. We then outline directions for future research.

We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Our approach extends BERT by (1) masking contiguous random spans, rather than random tokens, and (2) training the span boundary representations to predict the entire content of the masked span, without relying on the individual token representations within it. Span-BERT consistently outperforms BERT and our better-tuned baselines, with substantial gains on span selection tasks such as question answering and coreference resolution. In particular, with the same training data and model size as BERT large , our single model obtains 94.6% and 88.7% F1 on SQuAD 1.1 and 2.0 respectively. We also achieve a new state of the art on the OntoNotes coreference resolution task (79.6% F1), strong performance on the TACRED relation extraction benchmark, and even gains on GLUE. 1 * Equal contribution. 1 Our code and pre-trained models are available at https://github.com/facebookresearch/ SpanBERT.

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

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。

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.

Masked language modeling (MLM) pre-training methods such as BERT corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens. While they produce good results when transferred to downstream NLP tasks, they generally require large amounts of compute to be effective. As an alternative, we propose a more sample-efficient pre-training task called replaced token detection. Instead of masking the input, our approach corrupts it by replacing some tokens with plausible alternatives sampled from a small generator network. Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not. Thorough experiments demonstrate this new pre-training task is more efficient than MLM because the task is defined over all input tokens rather than just the small subset that was masked out. As a result, the contextual representations learned by our approach substantially outperform the ones learned by BERT given the same model size, data, and compute. The gains are particularly strong for small models; for example, we train a model on one GPU for 4 days that outperforms GPT (trained using 30x more compute) on the GLUE natural language understanding benchmark. Our approach also works well at scale, where it performs comparably to RoBERTa and XLNet while using less than 1/4 of their compute and outperforms them when using the same amount of compute.

Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language processing (NLP). The effectiveness of transfer learning has given rise to a diversity of approaches, methodology, and practice. In this paper, we explore the landscape of transfer learning techniques for NLP by introducing a unified framework that converts all text-based language problems into a text-to-text format. Our systematic study compares pre-training objectives, architectures, unlabeled data sets, transfer approaches, and other factors on dozens of language understanding tasks. By combining the insights from our exploration with scale and our new "Colossal Clean Crawled Corpus", we achieve state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. To facilitate future work on transfer learning for NLP, we release our data set, pre-trained models, and code.

大型神经模型的培训和推断很昂贵。但是，对于许多应用程序域，虽然新任务和模型经常出现，但建模的基础文档主要保持不变。我们研究如何通过嵌入回收利用（ER）来降低此类设置的计算成本：在执行训练或推理时从以前的模型中重新使用激活。与以前的工作相反，重点是冻结小型分类头进行填充，这通常会导致绩效显着下降，我们提出了从预告片的模型中缓存中间层的输出，并为新任务的剩余层进行填充。我们表明，我们的方法在训练过程中提供了100％的速度和55-86％的推理，并且对科学领域中文本分类和实体识别任务的准确性产生了可观的影响。对于通用域的问答任务，ER提供了类似的加速和少量准确性。最后，我们确定了ER的几个开放挑战和未来的方向。

用于预培训语言模型的自我监督学习的核心包括预训练任务设计以及适当的数据增强。语言模型中的大多数数据增强都是独立于上下文的。最近在电子中提出了一个开创性的增强，并通过引入辅助生成网络（发电机）来实现最先进的性能，以产生用于培训主要辨别网络（鉴别者）的上下文化数据增强。然而，这种设计引入了发电机的额外计算成本，并且需要调整发电机和鉴别器之间的相对能力。在本文中，我们提出了一种自增强策略（SAS），其中单个网络用于审视以后的时期的培训常规预训练和上下文化数据增强。基本上，该策略消除了单独的发电机，并使用单个网络共同执行具有MLM（屏蔽语言建模）和RTD（替换令牌检测）头的两个预训练任务。它避免了寻找适当大小的发电机的挑战，这对于在电子中证明的性能至关重要，以及其随后的变体模型至关重要。此外，SAS是一项常规策略，可以与最近或将来的许多新技术无缝地结合，例如杜伯塔省的解除关注机制。我们的实验表明，SAS能够在具有相似或更少的计算成本中优于胶水任务中的电磁和其他最先进的模型。

在这项工作中，我们探索如何学习专用的语言模型，旨在学习从文本文件中学习关键词的丰富表示。我们在判别和生成设置中进行预训练变压器语言模型（LMS）的不同掩蔽策略。在歧视性设定中，我们引入了一种新的预训练目标 - 关键边界，用替换（kbir）infifiling，在使用Kbir预先训练的LM进行微调时显示出在Sota上的性能（F1中高达9.26点）的大量增益关键酶提取的任务。在生成设置中，我们为BART - 键盘介绍了一个新的预训练设置，可再现与CATSeq格式中的输入文本相关的关键字，而不是Denoised原始输入。这也导致在关键词中的性能（F1 @ M）中的性能（高达4.33点），用于关键正版生成。此外，我们还微调了在命名实体识别（ner），问题应答（qa），关系提取（重新），抽象摘要和达到与SOTA的可比性表现的预训练的语言模型，表明学习丰富的代表关键词确实有利于许多其他基本的NLP任务。

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。

对于大多数自然语言处理任务，主要的实践是使用较小的下游数据集对大型预验证变压器模型（例如BERT）。尽管这种方法取得了成功，但尚不清楚这些收益在多大程度上归因于用于预处理而不是训练预处理的目标本身所采用的大量背景语料库。本文介绍了一项大规模的自我预测研究，其中相同的（下游）训练数据都用于预训练和填充。在解决Electra和Roberta型号以及10个不同下游数据集的实验中，我们观察到在BookWiki语料库上进行自我预测的竞争对手标准预告片（尽管使用了$ 10 \ times $ $ -500 \ times $ -500 \ times $少的数据），在7美元上以7美元的价格优于$ 7 $和$ 5 $数据集。令人惊讶的是，这些特定于任务的预预性模型通常在其他任务（包括胶水基准）上表现良好。我们的结果表明，在许多情况下，可归因于预处理的绩效收益主要是由预处理目标本身驱动的，并不总是归因于大规模数据集的合并。考虑到网络规模预处理数据中对知识产权和进攻内容的担忧，这些发现尤其重要。

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.

问题答案（QA）是自然语言处理中最具挑战性的最具挑战性的问题之一（NLP）。问答（QA）系统试图为给定问题产生答案。这些答案可以从非结构化或结构化文本生成。因此，QA被认为是可以用于评估文本了解系统的重要研究区域。大量的QA研究致力于英语语言，调查最先进的技术和实现最先进的结果。然而，由于阿拉伯QA中的研究努力和缺乏大型基准数据集，在阿拉伯语问答进展中的研究努力得到了很大速度的速度。最近许多预先接受的语言模型在许多阿拉伯语NLP问题中提供了高性能。在这项工作中，我们使用四个阅读理解数据集来评估阿拉伯QA的最先进的接种变压器模型，它是阿拉伯语 - 队，ArcD，AQAD和TYDIQA-GoldP数据集。我们微调并比较了Arabertv2基础模型，ArabertV0.2大型型号和ARAElectra模型的性能。在最后，我们提供了一个分析，了解和解释某些型号获得的低绩效结果。

The strong few-shot in-context learning capability of large pre-trained language models (PLMs) such as GPT-3 is highly appealing for application domains such as biomedicine, which feature high and diverse demands of language technologies but also high data annotation costs. In this paper, we present the first systematic and comprehensive study to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two highly representative biomedical information extraction tasks, named entity recognition and relation extraction. We follow the true few-shot setting to avoid overestimating models' few-shot performance by model selection over a large validation set. We also optimize GPT-3's performance with known techniques such as contextual calibration and dynamic in-context example retrieval. However, our results show that GPT-3 still significantly underperforms compared to simply fine-tuning a smaller PLM. In addition, GPT-3 in-context learning also yields smaller gains in accuracy when more training data becomes available. Our in-depth analyses further reveal issues of the in-context learning setting that may be detrimental to information extraction tasks in general. Given the high cost of experimenting with GPT-3, we hope our study provides guidance for biomedical researchers and practitioners towards more promising directions such as fine-tuning small PLMs.

Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We demonstrate that domain-specific pre-training offers performance improvements across all tasks. We release the benchmark to encourage future research in this domain.

由于表现强劲，预用的语言模型已成为许多NLP任务的标准方法，但他们培训价格昂贵。我们提出了一个简单高效的学习框架TLM，不依赖于大规模预制。给定一些标记的任务数据和大型常规语料库，TLM使用任务数据作为查询来检索一般语料库的微小子集，并联合优化任务目标和从头开始的语言建模目标。在四个域中的八个分类数据集上，TLM实现了比预用语言模型（例如Roberta-Light）更好地或类似的结果，同时减少了两个数量级的训练拖鞋。高精度和效率，我们希望TLM将有助于民主化NLP并加快发展。

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT with disentangled attention) that improves the BERT and RoBERTa models using two novel techniques. The first is the disentangled attention mechanism, where each word is represented using two vectors that encode its content and position, respectively, and the attention weights among words are computed using disentangled matrices on their contents and relative positions, respectively. Second, an enhanced mask decoder is used to incorporate absolute positions in the decoding layer to predict the masked tokens in model pre-training. In addition, a new virtual adversarial training method is used for fine-tuning to improve models' generalization. We show that these techniques significantly improve the efficiency of model pre-training and the performance of both natural language understand (NLU) and natural langauge generation (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model trained on half of the training data performs consistently better on a wide range of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%), on SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%). Notably, we scale up DeBERTa by training a larger version that consists of 48 Transform layers with 1.5 billion parameters. The significant performance boost makes the single DeBERTa model surpass the human performance on the SuperGLUE benchmark (Wang et al., 2019a) for the first time in terms of macro-average score (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the SuperGLUE leaderboard as of January 6, 2021, outperforming the human baseline by a decent margin (90.3 versus 89.8). The pre-trained DeBERTa models and the source code were released at: https://github.com/microsoft/DeBERTa 1 .