特定于语言的预训练模型已被证明比单语说在单语法评估设置中更准确,阿拉伯语也不例外。但是,我们发现先前发布的阿拉伯伯特模型显着培训。在这本技术报告中,我们展示了Jaber,Junior Arabic Bert,我们的预用语言模型原型专用于阿拉伯语。我们进行实证研究,以系统地评估模型在各种现有阿拉伯语NLU任务中的性能。实验结果表明,Jaber实现了Alue的最先进的表演,这是阿拉伯语了解评估的新基准,以及成熟的内部基准
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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。
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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。
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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.
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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。
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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).
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The use of multilingual language models for tasks in low and high-resource languages has been a success story in deep learning. In recent times, Arabic has been receiving widespread attention on account of its dialectal variance. While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants. It remains an open problem on whether the limited dialectical data can be used to improve the models trained in Arabic on its dialectal variants. First, we show that multilingual-BERT (mBERT) incrementally pretrained on Arabic monolingual data takes less training time and yields comparable accuracy when compared to our custom monolingual Arabic model and beat existing models (by an avg metric of +$6.41$). We then explore two continual pre-training methods-- (1) using small amounts of dialectical data for continual finetuning and (2) parallel Arabic to English data and a Translation Language Modeling loss function. We show that both approaches help improve performance on dialectal classification tasks ($+4.64$ avg. gain) when used on monolingual models.
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BERT,ROBERTA或GPT-3等复杂的基于注意力的语言模型的外观已允许在许多场景中解决高度复杂的任务。但是,当应用于特定域时,这些模型会遇到相当大的困难。诸如Twitter之类的社交网络就是这种情况,Twitter是一种不断变化的信息流,以非正式和复杂的语言编写的信息流,鉴于人类的重要作用,每个信息都需要仔细评估,即使人类也需要理解。通过自然语言处理解决该领域的任务涉及严重的挑战。当将强大的最先进的多语言模型应用于这种情况下,特定语言的细微差别用来迷失翻译。为了面对这些挑战,我们提出了\ textbf {bertuit},这是迄今为止针对西班牙语提出的较大变压器,使用Roberta Optimization进行了230m西班牙推文的大规模数据集进行了预培训。我们的动机是提供一个强大的资源,以更好地了解西班牙Twitter,并用于专注于该社交网络的应用程序,特别强调致力于解决该平台中错误信息传播的解决方案。对Bertuit进行了多个任务评估,并与M-Bert,XLM-Roberta和XLM-T进行了比较,该任务非常具有竞争性的多语言变压器。在这种情况下,使用应用程序显示了我们方法的实用性:一种可视化骗局和分析作者群体传播虚假信息的零击方法。错误的信息在英语以外的其他语言等平台上疯狂地传播,这意味着在英语说话之外转移时,变形金刚的性能可能会受到影响。
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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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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.
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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
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Given the impact of language models on the field of Natural Language Processing, a number of Spanish encoder-only masked language models (aka BERTs) have been trained and released. These models were developed either within large projects using very large private corpora or by means of smaller scale academic efforts leveraging freely available data. In this paper we present a comprehensive head-to-head comparison of language models for Spanish with the following results: (i) Previously ignored multilingual models from large companies fare better than monolingual models, substantially changing the evaluation landscape of language models in Spanish; (ii) Results across the monolingual models are not conclusive, with supposedly smaller and inferior models performing competitively. Based on these empirical results, we argue for the need of more research to understand the factors underlying them. In this sense, the effect of corpus size, quality and pre-training techniques need to be further investigated to be able to obtain Spanish monolingual models significantly better than the multilingual ones released by large private companies, specially in the face of rapid ongoing progress in the field. The recent activity in the development of language technology for Spanish is to be welcomed, but our results show that building language models remains an open, resource-heavy problem which requires to marry resources (monetary and/or computational) with the best research expertise and practice.
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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.
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大型预用屏蔽语言模型已成为许多NLP问题的最先进的解决方案。虽然研究表明,单晶模型产生比多语言模型产生更好的结果,但训练数据集必须足够大。我们培训了立陶宛,拉脱维亚语和英语的三种语言Litlat Bert样模型,以及爱沙尼亚的单语Est-Roberta模型。我们在四个下游任务中评估它们的性能:命名实体识别,依赖解析,词语标记和单词类比。为了分析对单一语言的重要性以及大型培训集的重要性,我们将创建的模型与爱沙尼亚,拉脱维亚和立陶宛人进行了现有的单语和多语言伯特模型。结果表明,新创建的Litlat Bert和Est-Roberta模型在大多数情况下改善了所有测试任务的现有模型的结果。
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与标准命名实体识别(NER)相比,在历史文本中识别人,位置和组织是一个巨大的挑战。为了获得机器可读的语料库,通常需要扫描历史文本,并且需要执行光学特征识别(OCR)。结果,历史文献包含错误。此外,位置或组织等实体可以随着时间的推移而改变,这构成了另一个挑战。总体而言,历史文本带有几种特殊性,这些特殊性与现代文本有很大不同,并且在该领域几乎无法使用训练神经标记器的大型标记的Corpora。在这项工作中,我们通过培训大型历史语言模型来解决历史,英语,法语,瑞典语和芬兰语的历史文献。我们通过使用未标记的数据预处理语言模型来规避大量标记数据的需求。我们提出了Hmbert,这是一种历史多语言基于BERT的语言模型,并以多种不同大小的版本发布该模型。此外,我们通过解决下游NER作为今年HIPE-2022共享任务的一部分来评估HMBERT的能力,并提供详细的分析和见解。对于多种语言的经典评论粗粒ner挑战,我们的标记者Histeria的表现优于其他团队的三种语言中的其他团队的模型。
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Pre-trained transformers are now the de facto models in Natural Language Processing given their state-of-the-art results in many tasks and languages. However, most of the current models have been trained on languages for which large text resources are already available (such as English, French, Arabic, etc.). Therefore, there are still a number of low-resource languages that need more attention from the community. In this paper, we study the Algerian dialect which has several specificities that make the use of Arabic or multilingual models inappropriate. To address this issue, we collected more than one million Algerian tweets, and pre-trained the first Algerian language model: DziriBERT. When compared with existing models, DziriBERT achieves better results, especially when dealing with the Roman script. The obtained results show that pre-training a dedicated model on a small dataset (150 MB) can outperform existing models that have been trained on much more data (hundreds of GB). Finally, our model is publicly available to the community.
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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。
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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 .
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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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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.
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