预计在现实世界中部署的NLU系统将定期更新或对随着时间的推移积累的新培训示例的基础神经网络进行重新更新。在我们的工作中，我们专注于多语言环境，在该环境中，我们希望在该设置中进一步捕获有关上述模型已经接受过培训的NLU任务的新培训数据的多语言模型。我们表明，在某些条件下，天真地更新多语言模型可能会导致语言子集的性能损失，尽管汇总性能指标显示出改进。我们在属于三个任务系列（令牌级，句子级别和SEQ2SEQ）的四个任务上建立了这种现象，并发现基线远非手头设置的理想选择。然后，我们基于最近进步的参数有效填充，以开发新颖的填充管道，使我们能够共同最大程度地减少灾难性的遗忘，同时鼓励积极的跨语言转移，从而改善不同语言的增长，同时减少这种设置中损失的损失。

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。

交叉语言语音适应旨在解决利用多种丰富资源语言来构建低资源目标语言的模型的问题。由于低资源语言具有有限的培训数据，语音识别模型可以容易地过度装备。在本文中，我们建议使用适配器来研究多种适配器的性能，用于参数有效的交叉语音语音适应。基于我们以前的MetaAdapter，隐含地利用适配器，我们提出了一种名为SimAdapter的新算法，用于从Adapters明确学习知识。我们的算法利用了可以轻松集成到变压器结构中的适配器.METAADAPTER利用元学习将一般知识从训练数据转移到测试语言。 SimAdapter旨在使用适配器微调期间了解源语言与目标语言之间的相似性。我们在公共语音数据集中对五种低资源语言进行广泛的实验。结果表明，与强大的全型微调基线相比，我们的MetaAdapter和SimAdapter方法可以将WER减小2.98％和2.55％，只有2.5％和15.5％的培训参数。此外，我们还表明这两种新型算法可以集成，以便更好的性能，相对减少高达3.55％。

与辅助语言的元学习已经表明了对交叉语言自然语言处理的有希望的改进。然而，以前的研究采样使用相同语言的元培训和元测试数据，这限制了模型交叉传输的能力。在本文中，我们提出了XLA-MAML，在元学习阶段执行直接交叉调整。我们对自然语言推理和问题进行零射击和几次拍摄实验。实验结果表明了我们在不同语言，任务和预磨料模型中的方法的有效性。我们还对元学习的各种交叉特定设置进行了分析，包括采样策略和并行性。

以前的工作主要侧重于改善NLU任务的交叉传输，具有多语言预用编码器（MPE），或提高与伯特的监督机器翻译的性能。然而，探索了，MPE是否可以有助于促进NMT模型的交叉传递性。在本文中，我们专注于NMT中的零射频转移任务。在此任务中，NMT模型培训，只有一个语言对的并行数据集和搁置架MPE，然后它直接测试在零拍语言对上。我们为此任务提出了Sixt，一个简单而有效的模型。 SIXT利用了两阶段培训计划利用MPE，并进一步改进了解离编码器和容量增强的解码器。使用此方法，SIMPT显着优于MBart，这是一个用于NMT的预磨削的多语言编码器解码器模型，平均改善了14个源语言的零拍摄的任何英语测试集上的7.1 BLEU。此外，培训计算成本和培训数据较少，我们的模型在15个任何英语测试组上实现了比Criss和M2M-100，两个强大的多语言NMT基线更好的性能。

多语言预训练的语言模型（PLM）在高资源和低资源语言的下游任务上表现出令人印象深刻的表现。但是，在预培训期间，尤其是非洲语言中，看不见的语言仍然有很大的表现。适应新语言的最有效方法之一是\ textit {语言自适应微调}（LAFT） - 使用预训练目标对单语言的多语言PLM进行微调。但是，适应目标语言会单独使用大磁盘空间，并限制了由此产生的模型的跨语言转移能力，因为它们已经专门用于单语言。在本文中，我们对17种最重要的非洲语言和其他三种在非洲大陆上广泛使用的高资源语言对17种最具资源的非洲语言进行\ Textit {多语言自适应微调}，以鼓励跨语性转移学习。为了进一步专注于多语言PLM，我们从嵌入式层中删除了与MAFT之前的非非洲写作脚本相对应的词汇令牌，从而将模型大小降低了约50％。我们对两个多语言PLM（Afriberta和XLM-R）和三个NLP任务（NER，新闻主题分类和情感分类）的评估表明，我们的方法可以在单个语言上应用LAFT，同时需要较小的磁盘空间。此外，我们表明我们的适应性PLM还提高了参数有效微调方法的零击跨语性转移能力。

将语义解析器定位以支持新语言需要有效的跨语性概括。最近的工作发现了机器翻译或零击方法的成功，尽管这些方法可能难以模拟母语人士如何提出问题。我们考虑如何有效利用新语言的最小注释示例来进行几次跨语性语义解析。我们引入了一阶元学习算法，以在跨语性转移过程中训练具有最大样品效率的语义解析器。我们的算法使用高资源语言来训练解析器，并同时优化低资源语言的跨语性概括。 ATIS上六种语言的结果表明，我们的泛化步骤的组合产生了准确的语义解析器，以每种新语言中的源培训数据$ 10％的$ 10％。我们的方法还使用英语对蜘蛛的竞争模型进行训练，并将其推广到中文，同样对$ 10％的培训数据进行了采样。

虽然审慎的语言模型（PLM）主要用作通用文本编码器，可以对各种下游任务进行微调，但最近的工作表明它们也可以重新连接以产生高质量的单词表示（即静态单词）嵌入）并在类型级词汇任务中产生良好的性能。虽然现有的工作主要集中在单语和双语环境中PLM的词汇专业化，但在这项工作中，我们将大规模多语言变压器（例如MMTS，例如Mbert或XLM-R）公开，以此为大规模的多语言词法知识，并利用Babelnet作为易于获得的丰富来源。多语言和跨语性类型级词汇知识。具体来说，我们利用Babelnet的多语言合成器来创建$ 50 $语言的同义词对，然后对MMTS（Mbert和XLM-R）进行对比目标指导的词汇专业化程序。我们表明，如此庞大的多语言词汇专业化为两项标准的跨语性词汇任务，双语词典感应和跨语性单词相似性以及跨语性句子检索带来了巨大的收益。至关重要的是，我们观察到在专业化中看不见的语言的收益，表明多语言词汇专业化使得概括无词法约束。在一系列随后的受控实验中，我们证明了MMT对专业化语言中单词表示的预处理质量对性能的影响要比一组约束集的语言多样性更大。令人鼓舞的是，这表明涉及低资源语言的词汇任务从资源丰富的语言的词汇知识中受益最大，通常更多。

多语种预训练模型在许多多语言NLP任务中展示了它们的有效性，并使从高资源语言到低资源的零射击或几秒钟传输。然而，由于某种语言之间的显着的类型差异和矛盾，这些模型通常在许多语言和交叉语言设置上表现不佳，这表明了学习单一模型同时处理大规模不同语言的难度。为了减轻这个问题，我们提出了一个新的多语言预训练管道。我们建议从多语言预先训练的模型产生语言表示，并进行语言分析，以表明语言表示相似度反映了从多个角度来看的语言相似度，包括语言家庭，地理蓝星，词汇表演和语法。然后，我们将所有目标语言集成到多个组中，并将每个组名称为表示SprachBund。因此，在同一表示SprachBund中的语言应该在培训和微调中互相提升，因为它们共享丰富的语言相似性。我们预先列车为每个代表斯普拉克班达一个多语言模型。实验在交叉基准上进行，与强基线相比，实现了显着的改进。

The BLOOM model is a large open-source multilingual language model capable of zero-shot learning, but its pretraining was limited to 46 languages. To improve its zero-shot performance on unseen languages, it is desirable to adapt BLOOM, but previous works have only explored adapting small language models. In this work, we apply existing language adaptation strategies to BLOOM and benchmark its zero-shot prompting performance on eight new languages. We find language adaptation to be effective at improving zero-shot performance in new languages. Surprisingly, adapter-based finetuning is more effective than continued pretraining for large models. In addition, we discover that prompting performance is not significantly affected by language specifics, such as the writing system. It is primarily determined by the size of the language adaptation data. We also add new languages to BLOOMZ, which is a multitask finetuned version of BLOOM capable of following task instructions zero-shot. We find including a new language in the multitask fine-tuning mixture to be the most effective method to teach BLOOMZ a new language. We conclude that with sufficient training data language adaptation can generalize well to diverse languages. Our code is available at \url{https://github.com/bigscience-workshop/multilingual-modeling/}.

Fine-tuning large pre-trained models is an effective transfer mechanism in NLP. However, in the presence of many downstream tasks, fine-tuning is parameter inefficient: an entire new model is required for every task. As an alternative, we propose transfer with adapter modules. Adapter modules yield a compact and extensible model; they add only a few trainable parameters per task, and new tasks can be added without revisiting previous ones. The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-art performance, whilst adding only a few parameters per task. On GLUE, we attain within 0.4% of the performance of full fine-tuning, adding only 3.6% parameters per task. By contrast, fine-tuning trains 100% of the parameters per task.

Multilingual machine translation suffers from negative interference across languages. A common solution is to relax parameter sharing with language-specific modules like adapters. However, adapters of related languages are unable to transfer information, and their total number of parameters becomes prohibitively expensive as the number of languages grows. In this work, we overcome these drawbacks using hyper-adapters -- hyper-networks that generate adapters from language and layer embeddings. While past work had poor results when scaling hyper-networks, we propose a rescaling fix that significantly improves convergence and enables training larger hyper-networks. We find that hyper-adapters are more parameter efficient than regular adapters, reaching the same performance with up to 12 times less parameters. When using the same number of parameters and FLOPS, our approach consistently outperforms regular adapters. Also, hyper-adapters converge faster than alternative approaches and scale better than regular dense networks. Our analysis shows that hyper-adapters learn to encode language relatedness, enabling positive transfer across languages.

随着时间的流逝，不断扩大知识并利用其快速推广到新任务的能力是人类语言智能的关键特征。然而，现有对新任务进行快速概括的模型（例如，很少的学习方法）主要是在固定数据集中的单个镜头中训练，无法动态扩展其知识；虽然不断学习算法并非专门设计用于快速概括。我们提出了一种新的学习设置，对几杆学习者（CLIF）的持续学习，以应对统一设置的两个学习设置的挑战。 CLIF假设模型从依次到达的一系列不同的NLP任务中学习，从而积累了知识，以改善对新任务的概括，同时还保留了较早所学的任务的性能。我们研究了在持续学习设置中如何影响概括能力，评估许多持续学习算法，并提出一种新型的正则适配器生成方法。我们发现，灾难性的遗忘影响着概括能力的程度远低于所见任务的表现。虽然持续学习算法仍然可以为概括能力带来可观的好处。

This paper considers continual learning of large-scale pretrained neural machine translation model without accessing the previous training data or introducing model separation. We argue that the widely used regularization-based methods, which perform multi-objective learning with an auxiliary loss, suffer from the misestimate problem and cannot always achieve a good balance between the previous and new tasks. To solve the problem, we propose a two-stage training method based on the local features of the real loss. We first search low forgetting risk regions, where the model can retain the performance on the previous task as the parameters are updated, to avoid the catastrophic forgetting problem. Then we can continually train the model within this region only with the new training data to fit the new task. Specifically, we propose two methods to search the low forgetting risk regions, which are based on the curvature of loss and the impacts of the parameters on the model output, respectively. We conduct experiments on domain adaptation and more challenging language adaptation tasks, and the experimental results show that our method can achieve significant improvements compared with several strong baselines.

在单独或多任务设置中评估了当前最新的视觉和语言模型，从而忽略了持续学习（CL）任务到达时的挑战。现有的CLENG分类促进了有关调整任务和减轻“灾难性遗忘”的研究，但仅限于仅视觉和仅语言的任务。我们提出了攀登，这是研究CL设置中学习多模式任务的挑战的基准，并系统地评估上游持续学习如何迅速概括为新的多模式和单峰任务。攀登包括几种CL算法的实现以及可以在多模式和单峰任务上部署的修改视觉语言变压器（VILT）模型。我们发现，常见的CL方法可以帮助减轻多模式任务学习期间的遗忘，但不要实现交叉任务知识转移。我们设想，攀登将有助于针对这种具有挑战性的多模式环境的新的CL算法进行研究。

Much recent progress in applications of machine learning models to NLP has been driven by benchmarks that evaluate models across a wide variety of tasks. However, these broad-coverage benchmarks have been mostly limited to English, and despite an increasing interest in multilingual models, a benchmark that enables the comprehensive evaluation of such methods on a diverse range of languages and tasks is still missing. To this end, we introduce the Cross-lingual TRansfer Evaluation of Multilingual Encoders (XTREME) benchmark, a multi-task benchmark for evaluating the cross-lingual generalization capabilities of multilingual representations across 40 languages and 9 tasks. We demonstrate that while models tested on English reach human performance on many tasks, there is still a sizable gap in the performance of cross-lingually transferred models, particularly on syntactic and sentence retrieval tasks. There is also a wide spread of results across languages. We release the benchmark 1 to encourage research on cross-lingual learning methods that transfer linguistic knowledge across a diverse and representative set of languages and tasks.

Prior work has shown that it is possible to expand pretrained Masked Language Models (MLMs) to new languages by learning a new set of embeddings, while keeping the transformer body frozen. Despite learning a small subset of parameters, this approach is not compute-efficient, as training the new embeddings requires a full forward and backward pass over the entire model. In this work, we propose mini-model adaptation, a compute-efficient alternative that builds a shallow mini-model from a fraction of a large model's parameters. New language-specific embeddings can then be efficiently trained over the mini-model, and plugged into the aligned large model for rapid cross-lingual transfer. We explore two approaches to learn mini-models: MiniJoint, which jointly pretrains the primary model and the mini-model using a single transformer with a secondary MLM head at a middle layer; and MiniPost, where we start from a regular pretrained model and build a mini-model by extracting and freezing a few layers and learning a small number of parameters on top. Experiments on XNLI, MLQA and PAWS-X show that mini-model adaptation matches the performance of the standard approach using up to 2.4x less compute.

最先进的神经（RE）排名者是众所周知的渴望数据，鉴于缺乏英语以外的其他语言培训数据 - 使它们很少用于多语言和跨语性检索设置。因此，当前的方法通常是通过多语言编码器培训的英语数据和跨语言设置的通常转移排名者：它们通过对英语相关性判断的所有预审预周化的多语言变压器（例如MMT，例如多语言BERT）的所有参数微调所有参数。用目标语言部署它们。在这项工作中，我们表明了两种参数效率的跨语性转移方法，即稀疏的微调蒙版（SFTM）和适配器，允许更轻巧，更有效的零拍传输到多语言和跨语言检索任务。我们首先通过蒙版语言建模来训练语言适配器（或SFTM），然后在最上方训练检索（即重新固定）适配器（SFTM），同时将所有其他参数保持固定。在推断时，这种模块化设计使我们能够通过应用（或SFTM）与源语言数据一起训练的（RE）排名适配器（或SFTM）以及目标语言的语言适配器（或SFTM）。我们对CLEF-2003和HC4基准进行了大规模的评估，此外，作为另一个贡献，我们还用三种新语言进行查询：吉尔吉斯，Uyghur和Turkish。所提出的参数效率方法的表现优于标准零射击传输，并具有完整的MMT微调，同时是模块化和减少训练时间。对于低资源语言，收益特别明显，我们的方法也大大优于基于竞争的机器翻译的排名。

A recent family of techniques, dubbed lightweight fine-tuning methods, facilitates parameter-efficient transfer learning by updating only a small set of additional parameters while keeping the parameters of the pretrained language model frozen. While proven to be an effective method, there are no existing studies on if and how such knowledge of the downstream fine-tuning approach should affect the pretraining stage. In this work, we show that taking the ultimate choice of fine-tuning method into consideration boosts the performance of parameter-efficient fine-tuning. By relying on optimization-based meta-learning using MAML with certain modifications for our distinct purpose, we prime the pretrained model specifically for parameter-efficient fine-tuning, resulting in gains of up to 1.7 points on cross-lingual NER fine-tuning. Our ablation settings and analyses further reveal that the tweaks we introduce in MAML are crucial for the attained gains.

Artificial neural networks thrive in solving the classification problem for a particular rigid task, acquiring knowledge through generalized learning behaviour from a distinct training phase. The resulting network resembles a static entity of knowledge, with endeavours to extend this knowledge without targeting the original task resulting in a catastrophic forgetting. Continual learning shifts this paradigm towards networks that can continually accumulate knowledge over different tasks without the need to retrain from scratch. We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries. Our main contributions concern (1) a taxonomy and extensive overview of the state-of-the-art; (2) a novel framework to continually determine the stability-plasticity trade-off of the continual learner; (3) a comprehensive experimental comparison of 11 state-of-the-art continual learning methods and 4 baselines. We empirically scrutinize method strengths and weaknesses on three benchmarks, considering Tiny Imagenet and large-scale unbalanced iNaturalist and a sequence of recognition datasets. We study the influence of model capacity, weight decay and dropout regularization, and the order in which the tasks are presented, and qualitatively compare methods in terms of required memory, computation time and storage.