自Mikolov等人的开创性工作以来。 （2013A）和Bojanowski等。 （2017），浅日志双线性语言模型的单词表示已成为许多NLP应用程序。 Mikolov等人。 （2018）介绍了一个位置日志双线性语言模型，具有基于关注的语言模型的特征，并且在内在单词类比任务上达到了最先进的性能。然而，位置模型从未评估了定性标准或外在任务，其速度是不切实际的。我们概述了注意机制与位置模型之间的相似性，并提出了一个受约束的位置模型，它适应Dai等人的稀疏注意机制。 （2018）。我们评估了三个新型定性标准的位置和受约束的位置模型及其对两种和布鲁森的外在语言建模任务（2014）。我们表明，位置和约束位置模型包含有关字令的可解释信息，优于Bojanowski等人的子字模型。 （2017）语言建模。我们还表明，受约束的位置模型优于语言建模的位置模型，并且是快速的两倍。

Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. This is a limitation, especially for languages with large vocabularies and many rare words. In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams. A vector representation is associated to each character n-gram; words being represented as the sum of these representations. Our method is fast, allowing to train models on large corpora quickly and allows us to compute word representations for words that did not appear in the training data. We evaluate our word representations on nine different languages, both on word similarity and analogy tasks. By comparing to recently proposed morphological word representations, we show that our vectors achieve state-of-the-art performance on these tasks.

The prevalent approach to sequence to sequence learning maps an input sequence to a variable length output sequence via recurrent neural networks. We introduce an architecture based entirely on convolutional neural networks. 1 Compared to recurrent models, computations over all elements can be fully parallelized during training to better exploit the GPU hardware and optimization is easier since the number of non-linearities is fixed and independent of the input length. Our use of gated linear units eases gradient propagation and we equip each decoder layer with a separate attention module. We outperform the accuracy of the deep LSTM setup of Wu et al. (2016) on both WMT'14 English-German and WMT'14 English-French translation at an order of magnitude faster speed, both on GPU and CPU.

我用Hunglish2语料库训练神经电脑翻译任务的模型。这项工作的主要贡献在培训NMT模型期间评估不同的数据增强方法。我提出了5种不同的增强方法，这些方法是结构感知的，这意味着而不是随机选择用于消隐或替换的单词，句子的依赖树用作增强的基础。我首先关于神经网络的详细文献综述，顺序建模，神经机翻译，依赖解析和数据增强。经过详细的探索性数据分析和Hunglish2语料库的预处理之后，我使用所提出的数据增强技术进行实验。匈牙利语的最佳型号达到了33.9的BLEU得分，而英国匈牙利最好的模型达到了28.6的BLEU得分。

The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens. We propose a novel simplified gating mechanism that outperforms Oord et al. (2016b) and investigate the impact of key architectural decisions. The proposed approach achieves state-of-the-art on the WikiText-103 benchmark, even though it features longterm dependencies, as well as competitive results on the Google Billion Words benchmark. Our model reduces the latency to score a sentence by an order of magnitude compared to a recurrent baseline. To our knowledge, this is the first time a non-recurrent approach is competitive with strong recurrent models on these large scale language tasks.

在本文中，我们试图通过引入深度学习模型的句法归纳偏见来建立两所学校之间的联系。我们提出了两个归纳偏见的家族，一个家庭用于选区结构，另一个用于依赖性结构。选区归纳偏见鼓励深度学习模型使用不同的单位（或神经元）分别处理长期和短期信息。这种分离为深度学习模型提供了一种方法，可以从顺序输入中构建潜在的层次表示形式，即更高级别的表示由高级表示形式组成，并且可以分解为一系列低级表示。例如，在不了解地面实际结构的情况下，我们提出的模型学会通过根据其句法结构组成变量和运算符的表示来处理逻辑表达。另一方面，依赖归纳偏置鼓励模型在输入序列中找到实体之间的潜在关系。对于自然语言，潜在关系通常被建模为一个定向依赖图，其中一个单词恰好具有一个父节点和零或几个孩子的节点。将此约束应用于类似变压器的模型之后，我们发现该模型能够诱导接近人类专家注释的有向图，并且在不同任务上也优于标准变压器模型。我们认为，这些实验结果为深度学习模型的未来发展展示了一个有趣的选择。

本教程展示了工作流程，将文本数据纳入精算分类和回归任务。主要重点是采用基于变压器模型的方法。平均长度为400个单词的车祸描述的数据集，英语和德语可用，以及具有简短财产保险索赔的数据集用来证明这些技术。案例研究应对与多语言环境和长输入序列有关的挑战。他们还展示了解释模型输出，评估和改善模型性能的方法，通过将模型调整到应用程序领域或特定预测任务。最后，该教程提供了在没有或仅有少数标记数据的情况下处理分类任务的实用方法。通过使用最少的预处理和微调的现成自然语言处理（NLP）模型的语言理解技能（NLP）模型实现的结果清楚地证明了用于实际应用的转移学习能力。

We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

近年来，基于变压器的预训练模型已获得了很大的进步，成为自然语言处理中最重要的骨干之一。最近的工作表明，变压器内部的注意力机制可能不需要，卷积神经网络和基于多层感知器的模型也已被研究为变压器替代方案。在本文中，我们考虑了一个用于语言模型预训练的图形循环网络，该网络通过本地令牌级通信为每个序列构建一个图形结构，以及与其他代币解耦的句子级表示。原始模型在受监督培训下的特定领域特定文本分类中表现良好，但是，其通过自我监督的方式学习转移知识的潜力尚未得到充分利用。我们通过优化体系结构并验证其在更通用的语言理解任务（英语和中文）中的有效性来填补这一空白。至于模型效率，我们的模型在基于变压器的模型中而不是二次复杂性，而是具有线性复杂性，并且在推断过程中的性能更有效。此外，我们发现与现有基于注意力的模型相比，我们的模型可以生成更多样化的输出，而背景化的功能冗余性较小。

The word alignment task, despite its prominence in the era of statistical machine translation (SMT), is niche and under-explored today. In this two-part tutorial, we argue for the continued relevance for word alignment. The first part provides a historical background to word alignment as a core component of the traditional SMT pipeline. We zero-in on GIZA++, an unsupervised, statistical word aligner with surprising longevity. Jumping forward to the era of neural machine translation (NMT), we show how insights from word alignment inspired the attention mechanism fundamental to present-day NMT. The second part shifts to a survey approach. We cover neural word aligners, showing the slow but steady progress towards surpassing GIZA++ performance. Finally, we cover the present-day applications of word alignment, from cross-lingual annotation projection, to improving translation.

We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network [23] but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings. It can also be seen as an extension of RNNsearch [2] to the case where multiple computational steps (hops) are performed per output symbol. The flexibility of the model allows us to apply it to tasks as diverse as (synthetic) question answering [22] and to language modeling. For the former our approach is competitive with Memory Networks, but with less supervision. For the latter, on the Penn TreeBank and Text8 datasets our approach demonstrates comparable performance to RNNs and LSTMs. In both cases we show that the key concept of multiple computational hops yields improved results.

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。

In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank.

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).

The rapid advancement of AI technology has made text generation tools like GPT-3 and ChatGPT increasingly accessible, scalable, and effective. This can pose serious threat to the credibility of various forms of media if these technologies are used for plagiarism, including scientific literature and news sources. Despite the development of automated methods for paraphrase identification, detecting this type of plagiarism remains a challenge due to the disparate nature of the datasets on which these methods are trained. In this study, we review traditional and current approaches to paraphrase identification and propose a refined typology of paraphrases. We also investigate how this typology is represented in popular datasets and how under-representation of certain types of paraphrases impacts detection capabilities. Finally, we outline new directions for future research and datasets in the pursuit of more effective paraphrase detection using AI.

In this work, we model abstractive text summarization using Attentional Encoder-Decoder Recurrent Neural Networks, and show that they achieve state-of-the-art performance on two different corpora. We propose several novel models that address critical problems in summarization that are not adequately modeled by the basic architecture, such as modeling key-words, capturing the hierarchy of sentence-toword structure, and emitting words that are rare or unseen at training time. Our work shows that many of our proposed models contribute to further improvement in performance. We also propose a new dataset consisting of multi-sentence summaries, and establish performance benchmarks for further research.

基于变压器的语言模型最近在许多自然语言任务中取得了显着的结果。但是，通常通过利用大量培训数据来实现排行榜的性能，并且很少通过将明确的语言知识编码为神经模型。这使许多人质疑语言学对现代自然语言处理的相关性。在本文中，我介绍了几个案例研究，以说明理论语言学和神经语言模型仍然相互关联。首先，语言模型通过提供一个客观的工具来测量语义距离，这对语言学家很有用，语义距离很难使用传统方法。另一方面，语言理论通过提供框架和数据源来探究我们的语言模型，以了解语言理解的特定方面，从而有助于语言建模研究。本论文贡献了三项研究，探讨了语言模型中语法 - 听觉界面的不同方面。在论文的第一部分中，我将语言模型应用于单词类灵活性的问题。我将Mbert作为语义距离测量的来源，我提供了有利于将单词类灵活性分析为方向过程的证据。在论文的第二部分中，我提出了一种方法来测量语言模型中间层的惊奇方法。我的实验表明，包含形态句法异常的句子触发了语言模型早期的惊喜，而不是语义和常识异常。最后，在论文的第三部分中，我适应了一些心理语言学研究，以表明语言模型包含了论证结构结构的知识。总而言之，我的论文在自然语言处理，语言理论和心理语言学之间建立了新的联系，以为语言模型的解释提供新的观点。

A goal of statistical language modeling is to learn the joint probability function of sequences of words. This is intrinsically difficult because of the curse of dimensionality: we propose to fight it with its own weapons.In the proposed approach one learns simultaneously (1) a distributed representation for each word (i.e. a similarity between words) along with (2) the probability function for word sequences, expressed with these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar to words forming an already seen sentence. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach very significantly improves on a state-of-the-art trigram model. "Y.B. was also with AT&T Research while doing this research.

建模法检索和检索作为预测问题最近被出现为法律智能的主要方法。专注于法律文章检索任务，我们展示了一个名为Lamberta的深度学习框架，该框架被设计用于民法代码，并在意大利民法典上专门培训。为了我们的知识，这是第一项研究提出了基于伯特（来自变压器的双向编码器表示）学习框架的意大利法律制度对意大利法律制度的高级法律文章预测的研究，最近引起了深度学习方法的增加，呈现出色的有效性在几种自然语言处理和学习任务中。我们通过微调意大利文章或其部分的意大利预先训练的意大利预先训练的伯爵来定义Lamberta模型，因为法律文章作为分类任务检索。我们Lamberta框架的一个关键方面是我们构思它以解决极端的分类方案，其特征在于课程数量大，少量学习问题，以及意大利法律预测任务的缺乏测试查询基准。为了解决这些问题，我们为法律文章的无监督标签定义了不同的方法，原则上可以应用于任何法律制度。我们提供了深入了解我们Lamberta模型的解释性和可解释性，并且我们对单一标签以及多标签评估任务进行了广泛的查询模板实验分析。经验证据表明了Lamberta的有效性，以及对广泛使用的深度学习文本分类器和一些构思的几次学习者来说，其优越性是对属性感知预测任务的优势。

我们介绍了块状变压器，该变压器以序列的反复方式应用变压器层，并且相对于序列长度具有线性复杂性。我们的复发单元在训练过程中在代币的块而不是单个令牌上运行，并利用块内并行计算，以便有效利用加速器硬件。单元本身非常简单。它仅仅是一个变压器层：它使用自我注意事项和交叉注意力来有效计算大量状态向量和令牌上的复发函数。我们的设计部分受到LSTM单元的启发，它使用LSTM风格的大门，但它可以将典型的LSTM单元缩放为几个数量级。我们的复发实现在计算时间和参数计数中都具有相同的成本作为传统的变压器层，但是在很长的序列中，语言建模任务中的语言建模任务的困惑极大地改善了。我们的模型比远程变压器XL基线的表现宽大，同时运行的速度是两倍。我们证明了它在PG19（书籍），Arxiv论文和GitHub源代码上的有效性。我们的代码已发布为开​​源。