Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions. Prior works on improving the logical reasoning ability of language models require complex processing of training data (e.g., aligning symbolic knowledge to text), yielding task-specific data augmentation solutions that restrict the learning of general logical reasoning skills. In this work, we propose APOLLO, an adaptively pretrained language model that has improved logical reasoning abilities. We select a subset of Wikipedia, based on a set of logical inference keywords, for continued pretraining of a language model. We use two self-supervised loss functions: a modified masked language modeling loss where only specific parts-of-speech words, that would likely require more reasoning than basic language understanding, are masked, and a sentence-level classification loss that teaches the model to distinguish between entailment and contradiction types of sentences. The proposed training paradigm is both simple and independent of task formats. We demonstrate the effectiveness of APOLLO by comparing it with prior baselines on two logical reasoning datasets. APOLLO performs comparably on ReClor and outperforms baselines on LogiQA.

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.

知识丰富的语言代表学习在各种知识密集型的NLP任务中表现出了有希望的表现。但是，现有的知识语言模型都培训了单格式知识图数据，这将其应用限制为更多语言。在这项工作中，我们向预先rain基于知识的多语言语言模型（KMLMS）提出了一种新颖的框架。我们首先使用Wikidata知识图来生成大量的代码切换合成句和基于推理的多语言训练数据。然后基于所生成的数据的内部和际际结构，我们设计预先升温任务，以促进知识学习，这允许语言模型不仅存储事实知识，还可以学习有用的逻辑模式。我们的预制kmlms展示了对广泛知识密集型的交叉线路任务的显着性能，包括指定实体识别，事实知识检索，关系分类以及我们设计的新任务，即逻辑推理。我们的代码和预付费语言模型将公开可用。

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。

蒙版语言建模（MLM）已被广泛用作培训前语言模型（PRLMS）中的剥夺目标。现有的PRLMS通常采用随机掩盖策略，在该策略中应用固定的掩蔽率，并且在整个培训中都有均等的概率掩盖了不同的内容。但是，该模型可能会受到训练前状态的复杂影响，随着训练时间的发展，这种影响会发生相应的变化。在本文中，我们表明这种时间不变的MLM设置对掩盖比和掩盖内容不太可能提供最佳结果，这激发了我们探索时间变化的MLM设置的影响。我们提出了两种计划的掩蔽方法，可在不同的训练阶段适应掩盖比和内容，从而提高了训练前效率和在下游任务上验证的效率。我们的工作是一项关于比率和内容的时间变化掩盖策略的先驱研究，并更好地了解掩盖比率和掩盖内容如何影响MLM的MLM预训练。

Pre-trained Language Model (PLM) has become a representative foundation model in the natural language processing field. Most PLMs are trained with linguistic-agnostic pre-training tasks on the surface form of the text, such as the masked language model (MLM). To further empower the PLMs with richer linguistic features, in this paper, we aim to propose a simple but effective way to learn linguistic features for pre-trained language models. We propose LERT, a pre-trained language model that is trained on three types of linguistic features along with the original MLM pre-training task, using a linguistically-informed pre-training (LIP) strategy. We carried out extensive experiments on ten Chinese NLU tasks, and the experimental results show that LERT could bring significant improvements over various comparable baselines. Furthermore, we also conduct analytical experiments in various linguistic aspects, and the results prove that the design of LERT is valid and effective. Resources are available at https://github.com/ymcui/LERT

大型基于变压器的预训练的语言模型在各种知识密集的任务上取得了令人印象深刻的表现，并可以在其参数中捕获事实知识。我们认为，考虑到不断增长的知识和资源需求，在模型参数中存储大量知识是亚最佳选择。我们认为，更有效的替代方法是向模型提供对上下文相关的结构化知识的明确访问，并训练它以使用该知识。我们提出了LM核 - 实现这一目标的一般框架 - 允许从外部知识源对语言模型培训的\ textit {解耦}，并允许后者更新而不会影响已经训练的模型。实验结果表明，LM核心获得外部知识，在知识探索任务上的最先进的知识增强语言模型中实现了重要而强大的优于性能。可以有效处理知识更新；并在两个下游任务上表现良好。我们还提出了一个彻底的错误分析，突出了LM核的成功和失败。

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。

尽管预训练的语言模型（PLM）已经在各种自然语言处理（NLP）任务上实现了最先进的表现，但在处理知识驱动的任务时，它们被证明缺乏知识。尽管为PLM注入知识做出了许多努力，但此问题仍然开放。为了应对挑战，我们提出\ textbf {dictbert}，这是一种具有词典知识增强PLM的新方法，比知识图（kg）更容易获取。在预训练期间，我们通过对比度学习将两个新颖的预训练任务注入plms：\ textit {dictionary输入预测}和\ textit {entriT {输入描述歧视}。在微调中，我们使用预先训练的dictbert作为插件知识库（KB）来检索输入序列中确定的条目的隐式知识，并将检索到的知识注入输入中，以通过新颖的额外跳动来增强其表示形式注意机制。我们评估了我们的方法，包括各种知识驱动和语言理解任务，包括NER，关系提取，CommonSenseQA，OpenBookQa和Glue。实验结果表明，我们的模型可以显着改善典型的PLM：它的大幅提高了0.5 \％，2.9 \％，9.0 \％，7.1 \％\％和3.3 \％，分别对Roberta--也有效大的。

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

事实证明，将先验知识纳入预训练的语言模型中对知识驱动的NLP任务有效，例如实体键入和关系提取。当前的培训程序通常通过使用知识掩盖，知识融合和知识更换将外部知识注入模型。但是，输入句子中包含的事实信息尚未完全开采，并且尚未严格检查注射的外部知识。结果，无法完全利用上下文信息，并将引入额外的噪音，或者注入的知识量受到限制。为了解决这些问题，我们提出了MLRIP，该MLRIP修改了Ernie-Baidu提出的知识掩盖策略，并引入了两阶段的实体替代策略。进行全面分析的广泛实验说明了MLRIP在军事知识驱动的NLP任务中基于BERT的模型的优势。

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.

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.

预训练的语言模型（PLM）在自然语言理解中的许多下游任务中取得了显着的性能增长。已提出了各种中文PLM，以学习更好的中文表示。但是，大多数当前模型都使用中文字符作为输入，并且无法编码中文单词中包含的语义信息。虽然最近的预训练模型同时融合了单词和字符，但它们通常会遭受不足的语义互动，并且无法捕获单词和字符之间的语义关系。为了解决上述问题，我们提出了一个简单而有效的PLM小扣手，该小扣子采用了对单词和性格表示的对比度学习。特别是，Clower通过对多透明信息的对比学习将粗粒的信息（即单词）隐式编码为细粒度表示（即字符）。在现实的情况下，小电动器具有很大的价值，因为它可以轻松地将其纳入任何现有的基于细粒的PLM中而无需修改生产管道。在一系列下游任务上进行的扩展实验表明，小动物的卓越性能超过了几个最先进的实验 - 艺术基线。

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。

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。

对中间标签数据任务（Stilts）的补充培训是一种广泛应用的技术，首先在感兴趣的目标任务之前精细调整在中间任务上的预训练语言模型。虽然Stilts能够进一步提高预用语言模型的性能，但目前还不清楚为什么和当它有效时。以前的研究表明，那些涉及复杂推理的中间任务，例如雄姐，适用于罗伯塔。在本文中，我们发现中间任务的改进可能与其含有推理或其他复杂技能的正交 - GPT2合成的简单实际识别任务可以受益各种目标任务。我们对研究不同因素对高跷的影响进行了广泛的实验。这些研究结果表明，重新思考中间微调在高跷管道中的作用。

Controllable Text Generation (CTG) is emerging area in the field of natural language generation (NLG). It is regarded as crucial for the development of advanced text generation technologies that are more natural and better meet the specific constraints in practical applications. In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text. However, due to the lower level of interpretability of deep neural networks, the controllability of these methods need to be guaranteed. To this end, controllable text generation using transformer-based PLMs has become a rapidly growing yet challenging new research hotspot. A diverse range of approaches have emerged in the recent 3-4 years, targeting different CTG tasks which may require different types of controlled constraints. In this paper, we present a systematic critical review on the common tasks, main approaches and evaluation methods in this area. Finally, we discuss the challenges that the field is facing, and put forward various promising future directions. To the best of our knowledge, this is the first survey paper to summarize CTG techniques from the perspective of PLMs. We hope it can help researchers in related fields to quickly track the academic frontier, providing them with a landscape of the area and a roadmap for future research.

Discriminative pre-trained language models (PLMs) learn to predict original texts from intentionally corrupted ones. Taking the former text as positive and the latter as negative samples, the PLM can be trained effectively for contextualized representation. However, the training of such a type of PLMs highly relies on the quality of the automatically constructed samples. Existing PLMs simply treat all corrupted texts as equal negative without any examination, which actually lets the resulting model inevitably suffer from the false negative issue where training is carried out on pseudo-negative data and leads to less efficiency and less robustness in the resulting PLMs. In this work, on the basis of defining the false negative issue in discriminative PLMs that has been ignored for a long time, we design enhanced pre-training methods to counteract false negative predictions and encourage pre-training language models on true negatives by correcting the harmful gradient updates subject to false negative predictions. Experimental results on GLUE and SQuAD benchmarks show that our counter-false-negative pre-training methods indeed bring about better performance together with stronger robustness.