Sentence simplification aims at making the structure of text easier to read and understand while maintaining its original meaning. This can be helpful for people with disabilities, new language learners, or those with low literacy. Simplification often involves removing difficult words and rephrasing the sentence. Previous research have focused on tackling this task by either using external linguistic databases for simplification or by using control tokens for desired fine-tuning of sentences. However, in this paper we purely use pre-trained transformer models. We experiment with a combination of GPT-2 and BERT models, achieving the best SARI score of 46.80 on the Mechanical Turk dataset, which is significantly better than previous state-of-the-art results. The code can be found at https://github.com/amanbasu/sentence-simplification.

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).

GPT-2和BERT展示了在各种自然语言处理任务上使用预训练的语言模型（LMS）的有效性。但是，在应用于资源丰富的任务时，LM微调通常会遭受灾难性的遗忘。在这项工作中，我们引入了一个协同的培训框架（CTNMT），该框架是将预训练的LMS集成到神经机器翻译（NMT）的关键。我们提出的CTNMT包括三种技术：a）渐近蒸馏，以确保NMT模型可以保留先前的预训练知识； b）动态的开关门，以避免灾难性忘记预训练的知识； c）根据计划的政策调整学习步伐的策略。我们在机器翻译中的实验表明，WMT14英语 - 德语对的CTNMT获得了最高3个BLEU得分，甚至超过了先前的最先进的预培训辅助NMT NMT的NMT。尽管对于大型WMT14英语法国任务，有400万句话，但我们的基本模型仍然可以显着改善最先进的变压器大型模型，超过1个BLEU得分。代码和模型可以从https://github.com/bytedance/neurst/tree/Master/Master/examples/ctnmt下载。

临床票据是记录患者信息的有效方法，但难以破译非专家的难以破译。自动简化医学文本可以使患者提供有关其健康的有价值的信息，同时节省临床医生。我们提出了一种基于词频率和语言建模的医学文本自动简化的新方法，基于富裕的外行术语的医疗本体。我们发布了一对公开可用的医疗句子的新数据集，并由临床医生简化了它们的版本。此外，我们定义了一种新颖的文本简化公制和评估框架，我们用于对我们对现有技术的方法进行大规模人类评估。我们基于在医学论坛数据上培训的语言模型的方法在保留语法和原始含义时产生更简单的句子，超越现有技术。

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。

近年来BERT显示明显的优势，在自然语言处理任务的巨大潜力。然而，培训和应用BERT需要计算上下文语言表示，这阻碍了它的普遍性和适用性密集的时间和资源。为了克服这个瓶颈，我们采用窗口屏蔽机制立正层提出了深刻的双向语言模型。这项工作计算上下文的语言表示，而没有随意屏蔽一样在BERT和保持深双向架构类似BERT。为了计算相同的句子表示，我们的方法显示出O（n）的复杂性相比少其他基于变压器的型号O（$ N ^ $ 2）。为了进一步显示其优越性，计算在CPU环境背景下的语言表述中进行，通过短信分类方面使用的嵌入，从所提出的方法，logistic回归显示更高的精度。 Moverover，所提出的方法也实现了语义相似任务显著更高的性能。

State-of-the-art text simplification (TS) systems adopt end-to-end neural network models to directly generate the simplified version of the input text, and usually function as a blackbox. Moreover, TS is usually treated as an all-purpose generic task under the assumption of homogeneity, where the same simplification is suitable for all. In recent years, however, there has been increasing recognition of the need to adapt the simplification techniques to the specific needs of different target groups. In this work, we aim to advance current research on explainable and controllable TS in two ways: First, building on recently proposed work to increase the transparency of TS systems, we use a large set of (psycho-)linguistic features in combination with pre-trained language models to improve explainable complexity prediction. Second, based on the results of this preliminary task, we extend a state-of-the-art Seq2Seq TS model, ACCESS, to enable explicit control of ten attributes. The results of experiments show (1) that our approach improves the performance of state-of-the-art models for predicting explainable complexity and (2) that explicitly conditioning the Seq2Seq model on ten attributes leads to a significant improvement in performance in both within-domain and out-of-domain settings.

We present BART, a denoising autoencoder for pretraining sequence-to-sequence models. BART is trained by ( 1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new stateof-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.

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。

This paper presents a new UNIfied pre-trained Language Model (UNILM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The unified modeling is achieved by employing a shared Transformer network and utilizing specific self-attention masks to control what context the prediction conditions on. UNILM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks. Moreover, UNILM achieves new state-ofthe-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the CoQA generative question answering F1 score to 82.5 (37.1 absolute improvement), the SQuAD question generation BLEU-4 to 22.12 (3.75 absolute improvement), and the DSTC7 document-grounded dialog response generation NIST-4 to 2.67 (human performance is 2.65). The code and pre-trained models are available at https://github.com/microsoft/unilm. * Equal contribution. † Contact person.

最新的语言建模工作表明，培训大型变压器模型促进了自然语言处理应用中的最新发展。但是，统一当前有效模型的工作很少。在这项工作中，我们使用当前有效的模型结构来启动通过当前大多数主流技术设置的模型。我们认为这将成为未来的基本模式。对于中文，使用GPT-2 [9]模型，在中国数据集中培训了103亿个参数语言模型，特别是培训基于对话数据的29亿参数语言模型; BERT Model在中国数据集上培训，参数为495万个参数;变压器模型在中文数据集中培训了具有56亿参数的语言模型。在英语中，也已经完成了相应的培训工作。使用GPT-2模型，英语数据集培训具有64亿参数的语言模型; BERT [3]模型在英国数据集中培训了具有124亿参数的语言模型，特别是根据单卡训练技术语言模型培训了688万参数;变压器模型在英语数据集中培训了具有56亿参数的语言模型。在CLUE [13]评估的TNews分类任务中，BERT-C模型超过了Albert-XXLarge的59.46％的精度，精度率为59.99％，增加0.53％。在胶水[11]评估的QQP分类任务中，精度率为78.95％超过了72.1％的精度率，增加了6.85％。与厄尼的当前精度率相比，胶水评价的第一名为75.2％，同比增长3.75％。

This report summarizes the work carried out by the authors during the Twelfth Montreal Industrial Problem Solving Workshop, held at Universit\'e de Montr\'eal in August 2022. The team tackled a problem submitted by CBC/Radio-Canada on the theme of Automatic Text Simplification (ATS).

Recent work has demonstrated substantial gains in pre-training large-scale unidirectional language models such as the GPT-2, GPT-3, and GPT-neo, followed by fine-tuning on a downstream task. In this paper, we evaluate the performance of the GPT-neo 1.3 billion model for commonsense reasoning tasks. We assess the model performance on six commonsense reasoning benchmark tasks and report the accuracy scores for these tasks. When fine-tuned using the right set of hyperparameters, we obtain competitive scores on three of these tasks but struggle when the dataset size is significantly smaller. The low model performance on a few of these tasks suggests the inherent difficulty in these datasets and since it fails to establish coherent patterns given their limited training samples. We also investigate and substantiate our results using visualization and conduct numerous inference tests to understand the model performance better. Finally, we conduct thorough robustness tests using various methods to gauge the model performance under numerous settings. These findings suggest a promising path for exploring smaller language models than the GPT-3 175 billion model to perform tasks requiring natural language understanding.

我们提出了一种简单有效的方法来产生各种释义，并在其中找到一个很好的质量释义。与之前的研究一样，很难确保一代方法总是在各个域中产生最佳的释义。因此，我们专注于找到来自多个候选者的最佳候选者，而不是假设只有一个生成模型和解码选项的组合。我们的方法表明，与先前的方法相比，它很容易应用于各个域，并且具有足够好的性能。此外，我们的方法可用于扩展下游语料库的数据增强，显示它可以帮助提高英语和韩语数据集的性能。

在任何翻译工作流程中，从源到目标的域知识保存至关重要。在翻译行业中，接收高度专业化的项目是很常见的，那里几乎没有任何平行的内域数据。在这种情况下，没有足够的内域数据来微调机器翻译（MT）模型，生成与相关上下文一致的翻译很具有挑战性。在这项工作中，我们提出了一种新颖的方法，用于域适应性，以利用最新的审计语言模型（LMS）来用于特定于域的MT的域数据增强，并模拟（a）的（a）小型双语数据集的域特征，或（b）要翻译的单语源文本。将这个想法与反翻译相结合，我们可以为两种用例生成大量的合成双语内域数据。为了进行调查，我们使用最先进的变压器体系结构。我们采用混合的微调来训练模型，从而显着改善了内域文本的翻译。更具体地说，在这两种情况下，我们提出的方法分别在阿拉伯语到英语对阿拉伯语言对上分别提高了大约5-6个BLEU和2-3 BLEU。此外，人类评估的结果证实了自动评估结果。

Bidirectional Encoder Representations from Transformers (BERT; Devlin et al. 2019) represents the latest incarnation of pretrained language models which have recently advanced a wide range of natural language processing tasks. In this paper, we showcase how BERT can be usefully applied in text summarization and propose a general framework for both extractive and abstractive models. We introduce a novel document-level encoder based on BERT which is able to express the semantics of a document and obtain representations for its sentences. Our extractive model is built on top of this encoder by stacking several intersentence Transformer layers. For abstractive summarization, we propose a new fine-tuning schedule which adopts different optimizers for the encoder and the decoder as a means of alleviating the mismatch between the two (the former is pretrained while the latter is not). We also demonstrate that a two-staged fine-tuning approach can further boost the quality of the generated summaries. Experiments on three datasets show that our model achieves stateof-the-art results across the board in both extractive and abstractive settings. 1

句子嵌入通常用于文本聚类和语义检索任务中。最先进的句子表示方法基于大量手动标记句子对集合的人工神经网络。高资源语言（例如英语或中文）可以使用足够数量的注释数据。在不太受欢迎的语言中，必须使用多语言模型，从而提供较低的性能。在本出版物中，我们通过提出一种培训有效的语言特定句子编码的方法来解决此问题，而无需手动标记数据。我们的方法是从句子对准双语文本语料库中自动构建释义对数据集。然后，我们使用收集的数据来微调具有附加复发池层的变压器语言模型。我们的句子编码器可以在不到一天的时间内在一张图形卡上进行培训，从而在各种句子级的任务上实现高性能。我们在波兰语中评估了八个语言任务的方法，并将其与最佳可用多语言句子编码器进行比较。

建筑聊天禁令的最大挑战是培训数据。所需的数据必须逼真，足以训练聊天禁止。我们创建一个工具，用于从Facebook页面的Facebook Messenger获取实际培训数据。在文本预处理步骤之后，新获得的数据集生成FVNC和示例数据集。我们使用返回越南（Phobert）的伯特来提取文本数据的功能。 K-means和DBSCAN聚类算法用于基于Phobert $ _ {Base} $的输出嵌入式群集任务。我们应用V测量分数和轮廓分数来评估聚类算法的性能。我们还展示了Phobert的效率与样本数据集和Wiki DataSet上的特征提取中的其他模型相比。还提出了一种结合聚类评估的GridSearch算法来找到最佳参数。由于群集如此多的对话，我们节省了大量的时间和精力来构建培训Chatbot的数据和故事情节。

定义生成任务旨在自动在特定上下文中生成一个单词的定义。但是，由于缺乏针对不同复杂性的数据集，模型产生的定义往往会保持相同的复杂度。本文提出了为具有可控复杂性级别的单词生成定义的新任务。相应地，我们介绍了编译，一个数据集给出了有关中国定义的详细信息，并且每个定义都标有其复杂性级别。编译数据集包括74,303个单词和106,882个定义。据我们所知，它是中国定义生成任务的最大数据集。我们选择各种代表性生成方法作为此任务的基准和进行评估，这说明我们的数据集在协助模型生成不同的复杂性级别定义方面发挥了出色的作用。我们认为，编译数据集将使复杂性可控定义生成的进一步研究受益。

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