We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

原型NLP实验训练了标记为英语数据的标准体系结构，并优化了准确性，而无需考虑其他方面，例如公平，解释性或计算效率。我们通过最近对NLP研究论文的手动分类表明，确实是这种情况，并将其称为正方形的实验设置。我们观察到，NLP研究通常超出了一个平方的设置，例如，不仅关注准确性，而且关注公平或解释性，而且通常仅沿着单个维度。例如，针对多语言的大多数工作仅考虑准确性；大多数关于公平或解释性的工作仅考虑英语；等等。我们通过对最近的NLP研究论文和ACL测试奖励获得者的手动分类来展示此信息。大多数研究的这种一维意味着我们只探索NLP研究搜索空间的一部分。我们提供了一个历史和最新示例，说明了一个偏见如何导致研究人员得出错误的结论或做出不明智的选择，指出了在研究歧管上有希望但未开发的方向，并提出实用的建议以实现更多的多维研究。我们打开注释的结果，以启用https://github.com/google-research/url-nlp的进一步分析

情感分析是NLP中研究最广泛的应用程序之一，但大多数工作都集中在具有大量数据的语言上。我们介绍了尼日利亚的四种口语最广泛的语言（Hausa，Igbo，Nigerian-Pidgin和Yor \'ub \'a）的第一个大规模的人类通知的Twitter情感数据集，该数据集由大约30,000个注释的推文组成（以及每种语言的大约30,000个）（以及14,000尼日利亚猎人），其中包括大量的代码混合推文。我们提出了文本收集，过滤，处理和标记方法，使我们能够为这些低资源语言创建数据集。我们评估了数据集上的预训练模型和转移策略。我们发现特定于语言的模型和语言适应性芬通常表现最好。我们将数据集，训练的模型，情感词典和代码释放到激励措施中，以代表性不足的语言进行情感分析。

数据增强是自然语言处理（NLP）模型的鲁棒性评估的重要组成部分，以及增强他们培训的数据的多样性。在本文中，我们呈现NL-Cogmenter，这是一种新的参与式Python的自然语言增强框架，它支持创建两个转换（对数据的修改）和过滤器（根据特定功能的数据拆分）。我们描述了框架和初始的117个变换和23个过滤器，用于各种自然语言任务。我们通过使用其几个转换来分析流行自然语言模型的鲁棒性来证明NL-Upmenter的功效。基础架构，Datacards和稳健性分析结果在NL-Augmenter存储库上公开可用（\ url {https://github.com/gem-benchmark/nl-augmenter}）。

尽管最近的多任务学习和自然语言处理的转移学习成功（NLP），但很少有效地研究了在训练中缩放任务数量的效果。迈出了这一目标，介绍了Exmix（极端混合物）：跨越各个领域和任务家庭的大规模收集107个监督的NLP任务。使用EXMIX，我们研究了最大规模的多任务预培训的影响，并分析了普通任务家庭之间的共同培训转移。通过此分析，我们表明手动策划用于多任务预训练的理想任务，并不简单，而且多任务缩放可以自行改进模型。最后，我们提出了Ext5：使用自我监督跨度去噪和监督EXMIX的多任务目标预先训练的模型。通过广泛的实验，我们表明Ext5优于超级格，宝石，彩虹，封闭书QA任务的强大T5基线，以及Exmix之外的几个任务。 Ext5在预训练时也显着提高了样品效率。

通过微调将大规模的预训练语言模型适应下游任务是实现NLP基准测试最先进性能的标准方法。然而，微调具有数百万或数十亿个参数的所有重量模型是对低资源设置中不稳定的采样低效，并且浪费，因为它需要为每个任务存储模型的单独副本。最近的工作已经开发了参数高效的微调方法，但这些方法仍然需要相对大量的参数或表现不足标准微调。在这项工作中，我们提出了一种特殊调整大型语言模型的方法，其在任务性能和比率参数之间具有更好的权衡的方法，而不是比上事先工作。 Compacter通过构建适配器，低级优化和参数化超复分乘法层的思想之上来实现这一目标。具体地，Compacter将特定于特定的权重矩阵插入到预估计模型的权重中，这些权重被有效地计算为共享的“慢速”权重和“快速”等级 - 每个Compacter层定义的矩阵之间的矩阵产品的总和。仅通过培训0.047％的预磨料模型的参数，Compacter会在胶水上标准微调和胜过标准微调的标准微调和低资源设置。我们的代码在〜\ url {https://github.com/rabeehk/compacter}上公开使用。

Transformers do not scale very well to long sequence lengths largely because of quadratic self-attention complexity. In the recent months, a wide spectrum of efficient, fast Transformers have been proposed to tackle this problem, more often than not claiming superior or comparable model quality to vanilla Transformer models. To this date, there is no well-established consensus on how to evaluate this class of models. Moreover, inconsistent benchmarking on a wide spectrum of tasks and datasets makes it difficult to assess relative model quality amongst many models. This paper proposes a systematic and unified benchmark, Long-Range Arena, specifically focused on evaluating model quality under long-context scenarios. Our benchmark is a suite of tasks consisting of sequences ranging from 1K to 16K tokens, encompassing a wide range of data types and modalities such as text, natural, synthetic images, and mathematical expressions requiring similarity, structural, and visual-spatial reasoning. We systematically evaluate ten well-established long-range Transformer models (Reformers, Linformers, Linear Transformers, Sinkhorn Transformers, Performers, Synthesizers, Sparse Transformers, and Longformers) on our newly proposed benchmark suite. Long-Range Arena paves the way towards better understanding this class of efficient Transformer models, facilitates more research in this direction, and presents new challenging tasks to tackle. Our benchmark code will be released at https://github.com/google-research/long-range-arena.

Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch. We propose Universal Language Model Fine-tuning (ULMFiT), an effective transfer learning method that can be applied to any task in NLP, and introduce techniques that are key for fine-tuning a language model. Our method significantly outperforms the state-of-the-art on six text classification tasks, reducing the error by 18-24% on the majority of datasets. Furthermore, with only 100 labeled examples, it matches the performance of training from scratch on 100× more data. We opensource our pretrained models and code 1 .

Logic Mill is a scalable and openly accessible software system that identifies semantically similar documents within either one domain-specific corpus or multi-domain corpora. It uses advanced Natural Language Processing (NLP) techniques to generate numerical representations of documents. Currently it leverages a large pre-trained language model to generate these document representations. The system focuses on scientific publications and patent documents and contains more than 200 million documents. It is easily accessible via a simple Application Programming Interface (API) or via a web interface. Moreover, it is continuously being updated and can be extended to text corpora from other domains. We see this system as a general-purpose tool for future research applications in the social sciences and other domains.

The analysis of network structure is essential to many scientific areas, ranging from biology to sociology. As the computational task of clustering these networks into partitions, i.e., solving the community detection problem, is generally NP-hard, heuristic solutions are indispensable. The exploration of expedient heuristics has led to the development of particularly promising approaches in the emerging technology of quantum computing. Motivated by the substantial hardware demands for all established quantum community detection approaches, we introduce a novel QUBO based approach that only needs number-of-nodes many qubits and is represented by a QUBO-matrix as sparse as the input graph's adjacency matrix. The substantial improvement on the sparsity of the QUBO-matrix, which is typically very dense in related work, is achieved through the novel concept of separation-nodes. Instead of assigning every node to a community directly, this approach relies on the identification of a separation-node set, which -- upon its removal from the graph -- yields a set of connected components, representing the core components of the communities. Employing a greedy heuristic to assign the nodes from the separation-node sets to the identified community cores, subsequent experimental results yield a proof of concept. This work hence displays a promising approach to NISQ ready quantum community detection, catalyzing the application of quantum computers for the network structure analysis of large scale, real world problem instances.