Climate change is threatening human health in unprecedented orders and many ways. These threats are expected to grow unless effective and evidence-based policies are developed and acted upon to minimize or eliminate them. Attaining such a task requires the highest degree of the flow of knowledge from science into policy. The multidisciplinary, location-specific, and vastness of published science makes it challenging to keep track of novel work in this area, as well as making the traditional knowledge synthesis methods inefficient in infusing science into policy. To this end, we consider developing multiple domain-specific language models (LMs) with different variations from Climate- and Health-related information, which can serve as a foundational step toward capturing available knowledge to enable solving different tasks, such as detecting similarities between climate- and health-related concepts, fact-checking, relation extraction, evidence of health effects to policy text generation, and more. To our knowledge, this is the first work that proposes developing multiple domain-specific language models for the considered domains. We will make the developed models, resources, and codebase available for the researchers.
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近年来,大型的语言模型(LM)彻底改变了自然语言处理(NLP)的领域。但是,虽然对通用语言进行了预测,但已证明对通用语言非常有效,但已经观察到利基语言会带来问题。特别是,与气候相关的文本包括常见LM无法准确表示的特定语言。我们认为,当今LMS的这种缺点限制了现代NLP对与气候相关文本的文本处理的广泛领域的适用性。作为一种补救措施,我们提出了Climatebert,这是一种基于变压器的语言模型,该模型在超过160万段的气候相关文本中进一步审议,这些文本涉及各种来源,例如普通新闻,研究文章和公司的气候报告。我们发现,在蒙版语言模型目标上,ClimateBertleads提高了46%的改善,这反过来又导致各种与气候相关的下游任务(如文本分类,情感分析和事实检查)的错误率降低了3.57%至35.71%。
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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.
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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。
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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。
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With an increasing amount of data in the art world, discovering artists and artworks suitable to collectors' tastes becomes a challenge. It is no longer enough to use visual information, as contextual information about the artist has become just as important in contemporary art. In this work, we present a generic Natural Language Processing framework (called ArtLM) to discover the connections among contemporary artists based on their biographies. In this approach, we first continue to pre-train the existing general English language models with a large amount of unlabelled art-related data. We then fine-tune this new pre-trained model with our biography pair dataset manually annotated by a team of professionals in the art industry. With extensive experiments, we demonstrate that our ArtLM achieves 85.6% accuracy and 84.0% F1 score and outperforms other baseline models. We also provide a visualisation and a qualitative analysis of the artist network built from ArtLM's outputs.
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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。
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社会科学的学术文献是记录人类文明并研究人类社会问题的文献。随着这种文献的大规模增长,快速找到有关相关问题的现有研究的方法已成为对研究人员的紧迫需求。先前的研究,例如SCIBERT,已经表明,使用特定领域的文本进行预训练可以改善这些领域中自然语言处理任务的性能。但是,没有针对社会科学的预训练的语言模型,因此本文提出了关于社会科学引文指数(SSCI)期刊上许多摘要的预培训模型。这些模型可在GitHub(https://github.com/s-t-full-text-knowledge-mining/ssci-bert)上获得,在学科分类和带有社会科学文学的抽象结构 - 功能识别任务方面表现出色。
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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
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The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can pro-vide a cheaper and superior performance than PLMs.
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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.
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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。
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使用现实世界数据的背景临床研究可能会受益于利用临床报告,这是一种特别丰富的非结构化培养基。为此,自然语言处理可以提取相关信息。使用预训练的语言模型基于转移学习的方法已在大多数NLP应用程序中实现了最先进的方法;但是,公开可用的模型缺乏接触专业语言,尤其是在医学领域。目标我们旨在评估将语言模型适应法国临床报告对下游医疗NLP任务的影响。方法我们利用从2017年8月至2021年7月在大巴黎大学医院(APHP)收集的2100万临床报告的语料库,以生产两种有关专业语言的卡梅蒙德体系结构:一项从Scratch中进行了再培训,另一个以Cammembert作为其初始化。我们使用两个法国注释的医学数据集将我们的语言模型与原始的Camembert网络进行比较,从而评估了Wilcoxon测试改进的统计意义。结果我们在临床报告上预估计的模型将APMED(APHP特定任务)的平均F1分数提高了3个百分点,达到91%,这是统计学上显着的改善。他们还达到了与Quaero上的原始Camembert相当的性能。这些结果适用于很少的预训练样品开始,从而对微调和划痕版本构成了这些结果。结论我们确认以前的文献表明,适应通才培训的语言模型(例如Camenbert on Specialty Corpora)改善了其下游临床NLP任务的性能。我们的结果表明,与微调相比,从头开始进行重新培训不会引起统计学上显着的性能增长。
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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.
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Language models pretrained on text from a wide variety of sources form the foundation of today's NLP. In light of the success of these broad-coverage models, we investigate whether it is still helpful to tailor a pretrained model to the domain of a target task. We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining indomain (domain-adaptive pretraining) leads to performance gains, under both high-and low-resource settings. Moreover, adapting to the task's unlabeled data (task-adaptive pretraining) improves performance even after domain-adaptive pretraining. Finally, we show that adapting to a task corpus augmented using simple data selection strategies is an effective alternative, especially when resources for domain-adaptive pretraining might be unavailable. Overall, we consistently find that multiphase adaptive pretraining offers large gains in task performance.
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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。
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Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text. Their analysis, which is at the center of legal practice, becomes increasingly elaborate as these collections grow in size. Natural language understanding (NLU) technologies can be a valuable tool to support legal practitioners in these endeavors. Their usefulness, however, largely depends on whether current state-of-the-art models can generalize across various tasks in the legal domain. To answer this currently open question, we introduce the Legal General Language Understanding Evaluation (LexGLUE) benchmark, a collection of datasets for evaluating model performance across a diverse set of legal NLU tasks in a standardized way. We also provide an evaluation and analysis of several generic and legal-oriented models demonstrating that the latter consistently offer performance improvements across multiple tasks.
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用于探索美国国家航空航天局的搜索工具(广告)可以相当丰富和赋予(例如,类似和趋势的运营商),但研究人员尚未允许完全杠杆语义搜索。例如,对“普朗克任务的结果”查询应该能够区分普朗克(人,任务,常量,机构和更多)的所有各种含义,而无需从用户进一步澄清。在广告中,我们正在将现代机器学习和自然语言处理技术应用于我们最近的天文出版物的数据集,以培训Astrobert,这是一种基于Google研究的深刻语境语言模型。使用AstrBert,我们的目标是丰富广告数据集并提高其可发现性,特别是我们正在开发自己的命名实体识别工具。我们在这里展示我们初步的结果和经验教训。
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当NLP模型从一个时间段进行文本数据培训并从另一个时间进行测试或部署或部署时,产生的时间未对准可能会降低结束任务性能。在这项工作中,我们在不同域名(社交媒体,科学论文,新闻和评论和评论)和时间(跨越五年或更长时间)的时间内建立了八个不同的任务套件,以量化时间未对准的影响。我们的研究专注于普遍存在的环境,其中佩戴的模型可选择通过持续的域特异性预测来改编,然后是特定于任务的FineTuning。我们在多个域中建立了一套任务,以研究现代NLP系统中的时间错位。我们发现对任务性能的时间不对准而不是先前报告的更强烈影响。我们还发现,虽然通过续预先训练的时间适应可以帮助,但与目标时间段中的数据上的任务特定的FineTuning相比,这些收益很小。我们的研究结果激励了提高NLP模型的时间稳健性的持续研究。
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时间是文档的重要方面,用于一系列NLP和IR任务。在这项工作中,我们研究了在预训练期间合并时间信息的方法,以进一步提高与时间相关的任务的性能。与Bert相比,使用同步文档收集(BooksCorpus和English Wikipedia)作为培训语料库相比,我们使用长跨度的时间新闻文章集合来构建单词表示。我们介绍了Timebert,这是一种新颖的语言表示模型,该模型通过两项新的预训练任务培训了新闻文章的临时收集,这些任务利用了两个不同的时间信号来构建时间认识的语言表示。实验结果表明,TimeBert始终胜过BERT和其他现有的预训练模型,在不同的下游NLP任务或应用程序上,时间很高的时间很重要。
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