绑架性自然语言推断（\ alpha {} nli）的任务是确定哪种假设是一组观察的可能性更可能的解释，是NLI的特别困难类型。与其仅仅确定因果关系，还需要常识，还需要评估解释的合理性。所有最新的竞争系统都以情境化表示为基础，并利用变压器体系结构来学习NLI模型。当某人面对特定的NLI任务时，他们需要选择可用的最佳模型。这是一项耗时且资源浓厚的努力。为了解决这个实用问题，我们提出了一种简单的方法来预测性能，而无需实际调整模型。我们通过测试预先训练的模型在\ alpha {} NLI任务上的性能如何，仅将具有余弦相似性的句子嵌入到训练这些嵌入式的分类器时所达到的性能。我们表明，余弦相似方法的准确性与Pearson相关系数为0.65的分类方法的准确性密切相关。由于相似性计算是在给定数据集上计算的数量级（少于一分钟与小时），因此我们的方法可以在模型选择过程中节省大量时间。

我们提供了从文本到文本变换器（T5）的第一次探索句子嵌入式。句子嵌入式广泛适用于语言处理任务。虽然T5在作为序列到序列映射问题的语言任务上实现令人印象深刻的性能，但目前尚不清楚如何从编码器解码器模型生成陈列嵌入的句子。我们调查三种方法提取T5句子嵌入方法：两个仅利用T5编码器，一个使用全T5编码器解码器模型。为了支持我们的调查，我们建立了一个新的句子代表转移基准，SentGlue，它将Senteval Toolkit扩展到粘合基准的九个任务。我们的编码器的型号优于Senteval和SentGlue传输任务的句子 - BERT和SIMCSE句子嵌入，包括语义文本相似性（STS）。发现从数百万到数十亿参数的缩放T5产生一致的进一步改进。最后，我们的编码器 - 解码器方法在使用句子嵌入时在STS上实现了新的最先进的。我们的模型在https://tfhub.dev/google/collections/sentence-t5/1发布。

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).

Cloze任务是一种广泛使用的任务，可以评估NLP系统的语言理解能力。然而，大多数现有的渗透任务只需要NLP系统以提供每个输入数据样本的相对最佳预测，而不是在输入域中以一致的方式以一致的方式为所有可能的预测的绝对质量。因此，提出了一种新的任务：预测填充任务中的填充词是一个好的，中立或坏候选者。可以扩展复杂的版本以预测更多离散类或连续分数。我们专注于Semoval 2022任务7的子任务，探讨了一些可能的架构来解决这一新任务，提供了对它们的详细比较，并提出了一种在这项新任务中改进传统模型的集合方法。

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.

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。

建议绑架自然语言推理任务（$ \ alpha $ NLI）以推断出原因与事件之间的最合理的解释。在$ \ Alpha $ NLI任务中，给出了两个观察，并要求最合理的假设从候选人中挑出。现有方法将每个候选假说之间的关系进行分别统一地惩罚推理网络。在本文中，我们认为不必区分正确假设之间的推理能力;同样，在解释观察的原因时，所有错误的假设都会有所贡献。因此，我们建议小组而不是排名假设和设计本文中称为“联合软制焦点”的结构损失。基于观察，假设通常与语义相关，我们设计了一种新颖的互动语言模型，旨在利用竞争假设之间丰富的互动。我们为$ \ alpha $ nli命名这个新型号：具有结构丢失（IMSL）的交互式模型。实验结果表明，我们的IMSL已经在罗伯塔大型预磨削模型上实现了最高性能，ACC和AUC结果分别增加了约1 \％和5 \％。

句子嵌入方法有许多成功的应用。但是，根据监督信号，在结果句子嵌入中捕获了哪些属性。在本文中，我们专注于具有相似体系结构和任务的两种类型的嵌入方法：一种关于自然语言推理任务的微型预训练的语言模型，以及其他微型训练的训练语言模型在单词预测任务上根据其定义句子，并研究其属性。具体而言，我们使用两个角度分区的STS数据集比较他们在语义文本相似性（STS）任务上的性能：1）句子源和2）句子对的表面相似性，并在下游和探测任务上比较其表现。此外，我们尝试结合两种方法，并证明将两种方法组合起来比无监督的STS任务和下游任务的各自方法的性能要好得多。

Language model pre-training has proven to be useful in learning universal language representations. As a state-of-the-art language model pre-training model, BERT (Bidirectional Encoder Representations from Transformers) has achieved amazing results in many language understanding tasks. In this paper, we conduct exhaustive experiments to investigate different fine-tuning methods of BERT on text classification task and provide a general solution for BERT fine-tuning. Finally, the proposed solution obtains new state-of-the-art results on eight widely-studied text classification datasets. 1

Using a single model across various tasks is beneficial for training and applying deep neural sequence models. We address the problem of developing generalist representations of text that can be used to perform a range of different tasks rather than being specialised to a single application. We focus on processing short questions and developing an embedding for these questions that is useful on a diverse set of problems, such as question topic classification, equivalent question recognition, and question answering. This paper introduces QBERT, a generalist model for processing questions. With QBERT, we demonstrate how we can train a multi-task network that performs all question-related tasks and has achieved similar performance compared to its corresponding single-task models.

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 .

BERT (Devlin et al., 2018) and RoBERTa has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that both sentences are fed into the network, which causes a massive computational overhead: Finding the most similar pair in a collection of 10,000 sentences requires about 50 million inference computations (~65 hours) with BERT. The construction of BERT makes it unsuitable for semantic similarity search as well as for unsupervised tasks like clustering.In this publication, we present Sentence-BERT (SBERT), a modification of the pretrained BERT network that use siamese and triplet network structures to derive semantically meaningful sentence embeddings that can be compared using cosine-similarity. This reduces the effort for finding the most similar pair from 65 hours with BERT / RoBERTa to about 5 seconds with SBERT, while maintaining the accuracy from BERT.We evaluate SBERT and SRoBERTa on common STS tasks and transfer learning tasks, where it outperforms other state-of-the-art sentence embeddings methods. 1

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。

We present CodeBERT, a bimodal pre-trained model for programming language (PL) and natural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language code search, code documentation generation, etc. We develop Code-BERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both "bimodal" data of NL-PL pairs and "unimodal" data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing. 1

Current state-of-the-art approaches to text classification typically leverage BERT-style Transformer models with a softmax classifier, jointly fine-tuned to predict class labels of a target task. In this paper, we instead propose an alternative training objective in which we learn task-specific embeddings of text: our proposed objective learns embeddings such that all texts that share the same target class label should be close together in the embedding space, while all others should be far apart. This allows us to replace the softmax classifier with a more interpretable k-nearest-neighbor classification approach. In a series of experiments, we show that this yields a number of interesting benefits: (1) The resulting order induced by distances in the embedding space can be used to directly explain classification decisions. (2) This facilitates qualitative inspection of the training data, helping us to better understand the problem space and identify labelling quality issues. (3) The learned distances to some degree generalize to unseen classes, allowing us to incrementally add new classes without retraining the model. We present extensive experiments which show that the benefits of ante-hoc explainability and incremental learning come at no cost in overall classification accuracy, thus pointing to practical applicability of our proposed approach.

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.

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.

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。

近年来，在应用预训练的语言模型（例如Bert）上，取得了巨大进展，以获取信息检索（IR）任务。在网页中通常使用的超链接已被利用用于设计预训练目标。例如，超链接的锚文本已用于模拟查询，从而构建了巨大的查询文档对以进行预训练。但是，作为跨越两个网页的桥梁，尚未完全探索超链接的潜力。在这项工作中，我们专注于建模通过超链接连接的两个文档之间的关系，并为临时检索设计一个新的预训练目标。具体而言，我们将文档之间的关系分为四组：无链接，单向链接，对称链接和最相关的对称链接。通过比较从相邻组采样的两个文档，该模型可以逐渐提高其捕获匹配信号的能力。我们提出了一个渐进的超链接预测（{php}）框架，以探索预训练中超链接的利用。对两个大规模临时检索数据集和六个提问数据集的实验结果证明了其优于现有的预训练方法。

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.