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cs.CL 方向，今日共计13篇
QA|VQA|问答|对话(1篇)
【1】 Zero-Shot Open-Book Question Answering 标题：零杆开卷问答 链接：https://arxiv.org/abs/2111.11520
作者：Sia Gholami,Mehdi Noori 机构：Amazon Web Services, CA USA 摘要：开放式图书问答是问答任务的一个子集，系统的目标是在给定的一组文档（开放式图书）中查找答案和有关主题的公共知识。本文提出了一种从AmazonWebServices（AWS）技术文档语料库中回答自然语言问题的解决方案，其中没有特定领域的标记数据（zero-shot）。这些问题可以有是非非答案、简短答案、长篇答案或以上任意组合。此解决方案包括两步架构，其中检索器查找正确的文档，提取器在检索到的文档中查找答案。我们将根据AWS技术文档中的实际客户问题，为开卷QA引入一个新的测试数据集。在对几种基于抽取语言模型的信息检索系统和抽取器模型进行实验后，该解决方案试图在同一过程中找到是非非答案和文本答案。该模型在斯坦福问答数据集-团队（Rajpurkar等人，2016年）和自然问题（Kwiatkowski等人，2019年）数据集上进行训练。我们能够在没有特定领域训练的情况下实现49%的F1和39%的端到端精确比赛分数（EM）。 摘要：Open book question answering is a subset of question answering tasks where the system aims to find answers in a given set of documents (open-book) and common knowledge about a topic. This article proposes a solution for answering natural language questions from a corpus of Amazon Web Services (AWS) technical documents with no domain-specific labeled data (zero-shot). These questions can have yes-no-none answers, short answers, long answers, or any combination of the above. This solution comprises a two-step architecture in which a retriever finds the right document and an extractor finds the answers in the retrieved document. We are introducing a new test dataset for open-book QA based on real customer questions on AWS technical documentation. After experimenting with several information retrieval systems and extractor models based on extractive language models, the solution attempts to find the yes-no-none answers and text answers in the same pass. The model is trained on the The Stanford Question Answering Dataset - SQuAD (Rajpurkaret al., 2016) and Natural Questions (Kwiatkowski et al., 2019) datasets. We were able to achieve 49% F1 and 39% exact match score (EM) end-to-end with no domain-specific training.
机器翻译(1篇)
【1】 Boosting Neural Machine Translation with Dependency-Scaled Self-Attention Network 标题：基于依赖尺度自关注网络的神经机器翻译 链接：https://arxiv.org/abs/2111.11707
作者：Ru Peng,Nankai Lin,Yi Fang,Shengyi Jiang,Junbo Zhao 机构：Zhao, School of Computer Science, Zhejiang University, China, School of Information, Guangdong University of Technology, Guangzhou Key Laboratory of Multilingual Intelligent Processing, School of 摘要：神经机器翻译模型假设语法知识可以通过注意网络自动地从双语语料库中学习。然而，在弱监督下训练的注意网络实际上无法捕捉句子的深层结构。当然，我们希望引入外部语法知识来指导注意网络的学习。因此，我们提出了一种新的、无参数的、依赖性尺度的自我注意网络，它将显式句法依赖性集成到注意网络中，以消除注意分布的分散性。最后，提出了两种知识稀疏技术来防止模型过度拟合有噪声的句法依赖。对IWSLT14德语到英语和WMT16德语到英语翻译任务的实验和广泛分析验证了我们方法的有效性。 摘要：Neural machine translation model assumes that syntax knowledge can be learned from the bilingual corpus via attention network automatically. However, the attention network trained in weak supervision actually cannot capture the deep structure of the sentence. Naturally, we expect to introduce external syntax knowledge to guide the learning of attention network. Thus, we propose a novel, parameter-free, dependency-scaled self-attention network, which integrate explicit syntactic dependencies into attention network to dispel the dispersion of attention distribution. Finally, two knowledge sparse techniques are proposed to prevent the model from overfitting noisy syntactic dependencies. Experiments and extensive analyses on the IWSLT14 German-to-English and WMT16 German-to-English translation tasks validate the effectiveness of our approach.
Graph|知识图谱|Knowledge(1篇)
【1】 Triple Classification for Scholarly Knowledge Graph Completion 标题：学术知识图补全的三重分类 链接：https://arxiv.org/abs/2111.11845
作者：Mohamad Yaser Jaradeh,Kuldeep Singh,Markus Stocker,Sören Auer 机构：L,S Research Center, Leibniz University Hannover, Cerence GmbH, Zerotha Research, Aachen, Germany, TIB Leibniz Information Centre for Science and, Technology, Hanover, Germany 摘要：学术知识图（KG）提供了丰富的结构化信息源，表示科学出版物中编码的知识。由于大量已出版的科学文献包含了大量描述科学概念的不均匀实体和关系，这些KG本质上是不完整的。我们提出了exBERT，一种利用预先训练好的transformer语言模型来完成学术知识图的方法。我们将知识图的三元组建模为文本，并执行三元组分类（即，是否属于KG）。评估表明，在三重分类、链接预测和关系预测任务方面，exBERT在三个学术KG完成数据集上优于其他基线。此外，我们还提供了两个学术数据集作为研究社区的资源，这些数据集来自公共KG和在线资源。 摘要：Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications. With the sheer volume of published scientific literature comprising a plethora of inhomogeneous entities and relations to describe scientific concepts, these KGs are inherently incomplete. We present exBERT, a method for leveraging pre-trained transformer language models to perform scholarly knowledge graph completion. We model triples of a knowledge graph as text and perform triple classification (i.e., belongs to KG or not). The evaluation shows that exBERT outperforms other baselines on three scholarly KG completion datasets in the tasks of triple classification, link prediction, and relation prediction. Furthermore, we present two scholarly datasets as resources for the research community, collected from public KGs and online resources.
摘要|信息提取(1篇)
【1】 TWEETSUMM -- A Dialog Summarization Dataset for Customer Service 链接：https://arxiv.org/abs/2111.11894
作者：Guy Feigenblat,Chulaka Gunasekara,Benjamin Sznajder,Sachindra Joshi,David Konopnicki,Ranit Aharonov 机构：IBM Research AI 备注：None 摘要：在典型的客户服务聊天场景中，客户联系支持中心寻求帮助或提出投诉，人工代理尝试解决问题。在大多数情况下，在谈话结束时，代理人被要求写一份简短的摘要，强调问题和建议的解决方案，通常是为了其他可能必须处理同一客户或问题的代理人的利益。本文的目标是推进此任务的自动化。我们介绍了第一个大规模、高质量、客户关怀对话摘要数据集，该数据集包含近6500个人工注释摘要。数据基于真实的客户支持对话框，包括摘录和抽象摘要。我们还介绍了一种新的针对对话的无监督抽取摘要方法。 摘要：In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.
推理|分析|理解|解释(1篇)
【1】 Visual Sentiment Analysis: A Natural DisasterUse-case Task at MediaEval 2021 标题：视觉情感分析：2021年中世纪自然灾害用例任务 链接：https://arxiv.org/abs/2111.11471
作者：Syed Zohaib Hassan,Kashif Ahmad,Michael A. Riegler,Steven Hicks,Nicola Conci,Paal Halvorsen,Ala Al-Fuqaha 机构：SimulaMet, Norway, Information and Computing Technology (ICT) Division, College of Science and Engineering, Hamad Bin Khalifa, University, Doha , Qatar, University of Trento, Italy 备注：3 pages 摘要：视觉情感分析任务是中世纪首次提供的。这项任务的主要目的是预测对社交媒体上共享的自然灾害图像的情绪反应。与灾难相关的图像通常比较复杂，通常会引起情绪反应，这使它们成为视觉情绪分析的理想用例。我们认为，能够对自然灾害相关数据进行有意义的分析可能具有重大的社会意义，在这方面的共同努力可以为未来的研究开辟几个有趣的方向。该任务由三个子任务组成，每个子任务旨在探索挑战的不同方面。在本文中，我们提供了任务的详细概述、任务的一般动机，以及用于评估所提议解决方案的数据集和指标的概述。 摘要：The Visual Sentiment Analysis task is being offered for the first time at MediaEval. The main purpose of the task is to predict the emotional response to images of natural disasters shared on social media. Disaster-related images are generally complex and often evoke an emotional response, making them an ideal use case of visual sentiment analysis. We believe being able to perform meaningful analysis of natural disaster-related data could be of great societal importance, and a joint effort in this regard can open several interesting directions for future research. The task is composed of three sub-tasks, each aiming to explore a different aspect of the challenge. In this paper, we provide a detailed overview of the task, the general motivation of the task, and an overview of the dataset and the metrics to be used for the evaluation of the proposed solutions.
GAN|对抗|攻击|生成相关(1篇)
【1】 Generating GPU Compiler Heuristics using Reinforcement Learning 标题：基于强化学习的GPU编译器启发式生成 链接：https://arxiv.org/abs/2111.12055
作者：Ian Colbert,Jake Daly,Norm Rubin 机构：Advanced Micro Devices, Inc. 摘要：GPU编译器是复杂的软件程序，针对目标硬件进行了许多优化。这些优化通常由编译器专家使用时间和资源密集型流程手工设计的启发式算法控制。在本文中，我们开发了一个GPU编译器自动调整框架，该框架使用非策略深度强化学习生成启发式，以提高图形应用程序的帧速率。此外，我们还通过分析它们在一年的代码签入过程中的稳定性（无需重新训练），展示了这些学习到的启发式算法对频繁的编译器更新的弹性。我们展示了我们基于机器学习的编译器自动调整框架能够匹配或超过98%的图形基准的帧速率，平均提升1.6%到15.8%。 摘要：GPU compilers are complex software programs with many optimizations specific to target hardware. These optimizations are often controlled by heuristics hand-designed by compiler experts using time- and resource-intensive processes. In this paper, we developed a GPU compiler autotuning framework that uses off-policy deep reinforcement learning to generate heuristics that improve the frame rates of graphics applications. Furthermore, we demonstrate the resilience of these learned heuristics to frequent compiler updates by analyzing their stability across a year of code check-ins without retraining. We show that our machine learning-based compiler autotuning framework matches or surpasses the frame rates for 98% of graphics benchmarks with an average uplift of 1.6% up to 15.8%.
半/弱/无监督|不确定性(1篇)
【1】 DABS: A Domain-Agnostic Benchmark for Self-Supervised Learning 标题：DABS：一种与领域无关的自我监督学习基准 链接：https://arxiv.org/abs/2111.12062
作者：Alex Tamkin,Vincent Liu,Rongfei Lu,Daniel Fein,Colin Schultz,Noah Goodman 机构：Stanford University 备注：NeurIPS 2021, Datasets & Benchmarks Track 摘要：自监督学习算法，包括BERT和SimCLR，在自然语言处理、计算机视觉和语音处理等领域取得了重大进展。然而，这些算法是特定于领域的，这意味着必须为每个新设置开发新的自监督学习算法，包括无数医疗、科学和多模式领域。为了促进领域不可知方法的发展，我们引入了DABS：一种用于自监督学习的领域不可知基准。为了在DABS上表现良好，我们在七个不同的领域对算法进行了评估：自然图像、多通道传感器数据、英语文本、语音记录、多语言文本、胸部x射线和带有文本描述的图像。每个域包含一个用于预训练的未标记数据集；然后根据模型在域中一组标记任务上的下游性能对模型进行评分。我们还介绍了e-Mix和ShED：两种基线域不可知算法；他们相对温和的表现表明，在自监督学习成为任意领域的现成解决方案之前，需要取得重大进展。有关基准数据集和基准算法的代码，请访问https://github.com/alextamkin/dabs. 摘要：Self-supervised learning algorithms, including BERT and SimCLR, have enabled significant strides in fields like natural language processing, computer vision, and speech processing. However, these algorithms are domain-specific, meaning that new self-supervised learning algorithms must be developed for each new setting, including myriad healthcare, scientific, and multimodal domains. To catalyze progress toward domain-agnostic methods, we introduce DABS: a Domain-Agnostic Benchmark for Self-supervised learning. To perform well on DABS, an algorithm is evaluated on seven diverse domains: natural images, multichannel sensor data, English text, speech recordings, multilingual text, chest x-rays, and images with text descriptions. Each domain contains an unlabeled dataset for pretraining; the model is then is scored based on its downstream performance on a set of labeled tasks in the domain. We also present e-Mix and ShED: two baseline domain-agnostic algorithms; their relatively modest performance demonstrates that significant progress is needed before self-supervised learning is an out-of-the-box solution for arbitrary domains. Code for benchmark datasets and baseline algorithms is available at https://github.com/alextamkin/dabs.
识别/分类(1篇)
【1】 Romanian Speech Recognition Experiments from the ROBIN Project 标题：罗宾计划的罗马尼亚语音识别实验 链接：https://arxiv.org/abs/2111.12028
作者：Andrei-Marius Avram,Vasile Păiş,Dan Tufiş 机构：Research Institute for Artificial Intelligence, Romanian Academy 备注：12 pages, 3 figures, ConsILR2020 摘要：接受社会辅助机器人的基本功能之一是其与环境中其他代理的通信能力。在罗宾项目的背景下，研究了通过与机器人的语音交互进行情景对话。本文介绍了使用深度神经网络进行的不同语音识别实验，重点是在仍然可靠的情况下产生快速（网络本身的延迟小于100ms）的模型。尽管关键的期望特征之一是低延迟，但最终的深度神经网络模型实现了识别罗马尼亚语言的最新结果，与语言模型结合时，获得了9.91%的单词错误率（WER），因此，在提供改进的运行时性能的同时，改进了先前的结果。此外，我们还探讨了两个用于纠正ASR输出的模块（连字符和大写字母恢复以及未知单词纠正），目标是ROBIN项目的目标（封闭微世界中的对话）。我们设计了一个基于API的模块化架构，允许集成引擎（机器人内部或外部）根据需要将可用模块链接在一起。最后，我们将所提出的设计集成到RELATE平台中，并通过上传文件或录制新语音向web用户提供ASR服务，从而对该设计进行测试。 摘要：One of the fundamental functionalities for accepting a socially assistive robot is its communication capabilities with other agents in the environment. In the context of the ROBIN project, situational dialogue through voice interaction with a robot was investigated. This paper presents different speech recognition experiments with deep neural networks focusing on producing fast (under 100ms latency from the network itself), while still reliable models. Even though one of the key desired characteristics is low latency, the final deep neural network model achieves state of the art results for recognizing Romanian language, obtaining a 9.91% word error rate (WER), when combined with a language model, thus improving over the previous results while offering at the same time an improved runtime performance. Additionally, we explore two modules for correcting the ASR output (hyphen and capitalization restoration and unknown words correction), targeting the ROBIN project's goals (dialogue in closed micro-worlds). We design a modular architecture based on APIs allowing an integration engine (either in the robot or external) to chain together the available modules as needed. Finally, we test the proposed design by integrating it in the RELATE platform and making the ASR service available to web users by either uploading a file or recording new speech.
其他神经网络|深度学习|模型|建模(3篇)
【1】 CL-NERIL: A Cross-Lingual Model for NER in Indian Languages 标题：CL-NERIL：一种面向印度语言的跨语言NER模型 链接：https://arxiv.org/abs/2111.11815
作者：Akshara Prabhakar,Gouri Sankar Majumder,Ashish Anand 机构：National Institute of Technology Karnataka, Surathkal, Indian Institute of Technology Guwahati 备注：Accepted in AAAI 2022 Student Abstract 摘要：为印度语言开发命名实体识别（NER）系统是一项长期的挑战，主要是因为需要大量带注释的干净训练实例。本文通过利用英语和印度语言的平行语料库和英语NER数据集，提出了一种低资源环境下印度语言NER的端到端框架。该框架包括一种注释投影方法，该方法结合了源语言（英语）数据上的单词对齐得分和NER标记预测置信度得分，以生成目标印度语言中的弱标记数据。我们采用了师生模型的一种变体，并根据教师模型的伪标签和对生成的弱标签数据的预测对其进行联合优化。我们还提供了三种印度语言的手动注释测试集：印地语、孟加拉语和古吉拉特语。我们在三种印度语言的测试集上评估了该框架的性能。实证结果表明，在所有语言上，与Zero-Shot迁移学习模型相比，性能至少提高了10%。这表明，在目标印度语中使用建议的注释投影方法生成的弱标记数据可以补充注释良好的源语言数据，从而提高性能。我们的代码在https://github.com/aksh555/CL-NERIL 摘要：Developing Named Entity Recognition (NER) systems for Indian languages has been a long-standing challenge, mainly owing to the requirement of a large amount of annotated clean training instances. This paper proposes an end-to-end framework for NER for Indian languages in a low-resource setting by exploiting parallel corpora of English and Indian languages and an English NER dataset. The proposed framework includes an annotation projection method that combines word alignment score and NER tag prediction confidence score on source language (English) data to generate weakly labeled data in a target Indian language. We employ a variant of the Teacher-Student model and optimize it jointly on the pseudo labels of the Teacher model and predictions on the generated weakly labeled data. We also present manually annotated test sets for three Indian languages: Hindi, Bengali, and Gujarati. We evaluate the performance of the proposed framework on the test sets of the three Indian languages. Empirical results show a minimum 10% performance improvement compared to the zero-shot transfer learning model on all languages. This indicates that weakly labeled data generated using the proposed annotation projection method in target Indian languages can complement well-annotated source language data to enhance performance. Our code is publicly available at https://github.com/aksh555/CL-NERIL
【2】 S-SimCSE: Sampled Sub-networks for Contrastive Learning of Sentence Embedding 链接：https://arxiv.org/abs/2111.11750
作者：Junlei Zhang,Zhenzhong lan 机构：Westlake University 备注：2 pages 摘要：对比学习是为了提高句子嵌入学习的效果而进行的研究。目前最先进的方法是SimCSE，它将辍学作为一种数据增强方法，并将相同的输入语句两次提供给经过预训练的Transformer编码器。然后，从不同的退出掩码派生的两个句子嵌入可以构建一个正对。一个应用了丢包掩码的网络可以被视为其自身的子网络，其预期规模由丢包率决定。在本文中，我们认为对于同一个句子，大多数具有不同期望尺度的子网络都可以学习相似的嵌入。SimCSE未能做到这一点，因为他们将辍学率固定为调整后的值，而我们对每个辍学函数的辍学率进行采样。由于这种方法会增加优化的难度，我们还提出了一种简单的句子屏蔽策略来对更多的子网络进行采样。我们在几个流行的语义文本相似性数据集上评估了所提出的S-SimCSE。实验结果表明，S-SimCSE在BERT基础上优于最先进的SimCSE超过$1%$。 摘要：Contrastive learning has been studied for improving the performance of sentence embedding learning. The current state-of-the-art method is the SimCSE, which takes dropout as a data augmentation method and feeds a pre-trained Transformer encoder the same input sentence twice. Then, two sentence embeddings derived from different dropout masks can get to build a positive pair. A network being applied a dropout mask can be regarded as a sub-network of itself, whose expected scale is determined by the dropout rate. In this paper, we push most sub-networks with different expected scales can learn similar embedding for the same sentence. SimCSE failed to do so because they fixed the dropout rate to a tuned value, while we sampled dropout rates for each of the dropout functions. As this method will increase the difficulties of optimization, we also propose a simple sentence-wise masks strategy to sample more sub-networks. We evaluated the proposed S-SimCSE on several popular semantic text similarity datasets. Experimental results show that S-SimCSE outperforms the state-of-the-art SimCSE more than $1%$ on BERT-base.
【3】 SpeechMoE2: Mixture-of-Experts Model with Improved Routing 标题：SpeechMoE2：改进路由的专家混合模型 链接：https://arxiv.org/abs/2111.11831
作者：Zhao You,Shulin Feng,Dan Su,Dong Yu 机构：Tencent AI Lab, Shenzhen, China, Tencent AI Lab, Bellevue, WA, USA 备注：5 pages, 1 figure. Submitted to ICASSP 2022 摘要：将基于专家的声学模型与动态路由机制相结合，在语音识别中取得了良好的效果。路由器结构的设计原则对于大模型容量和高计算效率至关重要。我们以前的工作SpeechMoE只使用局部图形嵌入来帮助路由器做出路由决策。为了进一步提高针对不同域和重音的语音识别性能，我们提出了一种新的路由器结构，该结构将额外的全局域和重音嵌入到路由器输入中，以提高适应性。实验结果表明，与SpeechMoE相比，SpeechMoE2在多域和多重音任务中都能获得更低的字符错误率（CER），且参数具有可比性。首先，该方法为多域任务和多重音任务分别提供了高达1.6%-4.8%的相对CER改进和1.9%-17.7%的相对CER改进。此外，增加专家数量还可以实现性能的持续改进，并保持计算成本不变。 摘要：Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition. The design principle of router architecture is important for the large model capacity and high computational efficiency. Our previous work SpeechMoE only uses local grapheme embedding to help routers to make route decisions. To further improve speech recognition performance against varying domains and accents, we propose a new router architecture which integrates additional global domain and accent embedding into router input to promote adaptability. Experimental results show that the proposed SpeechMoE2 can achieve lower character error rate (CER) with comparable parameters than SpeechMoE on both multi-domain and multi-accent task. Primarily, the proposed method provides up to 1.6% - 4.8% relative CER improvement for the multidomain task and 1.9% - 17.7% relative CER improvement for the multi-accent task respectively. Besides, increasing the number of experts also achieves consistent performance improvement and keeps the computational cost constant.
其他(2篇)
【1】 A bifurcation threshold for contact-induced language change 标题：一种接触诱发语言变化的分叉阈值 链接：https://arxiv.org/abs/2111.12061
作者：Henri Kauhanen 机构：University of Konstanz 备注：31 pages, 3 figures, 3 tables 摘要：一种被提出的语言变化机制涉及第二语言学习者在语言接触中所扮演的角色。如果一个语言社区中有足够多的第二语言使用者（相对于第一语言使用者的数量而言），那么那些在第二语言习得中存在困难的特征可能会从语言中消失。本文提出了一个基于强化学习和非线性动力学的此类接触情况的数学模型。充分刻画了描述母语和二语说话人混合群体的全随机模型的确定性约化的平衡。语言是否会随着第二语言学习者的引入而发生变化取决于三个因素：第二语言学习者在人群中的总体比例、所讨论的语言变体的相对优势以及说话者在作为第二语言习得语言时面临的困难程度。这些因素与一个数学公式有关，该公式描述了从保留第二语言困难特征到从两个说话人群体中消失的相变。这提供了可以根据经验数据进行检验的预测。在此，借助两个案例研究对该模型进行了评估，即南非荷兰语的形态均衡和非洲-秘鲁西班牙语中的空受试者侵蚀；该模型与两种情况下的历史发展基本一致。 摘要：One proposed mechanism of language change concerns the role played by second-language (L2) learners in situations of language contact. If sufficiently many L2 speakers are present in a speech community in relation to the number of first-language (L1) speakers, then those features which present a difficulty in L2 acquisition may be prone to disappearing from the language. This paper proposes a mathematical model of such contact situations based on reinforcement learning and nonlinear dynamics. The equilibria of a deterministic reduction of a full stochastic model, describing a mixed population of L1 and L2 speakers, are fully characterized. Whether or not the language changes in response to the introduction of L2 learners turns out to depend on three factors: the overall proportion of L2 learners in the population, the relative advantages of the linguistic variants in question, and the strength of the difficulty speakers face in acquiring the language as an L2. These factors are related by a mathematical formula describing a phase transition from retention of the L2-difficult feature to its loss from both speaker populations. This supplies predictions that can be tested against empirical data. Here, the model is evaluated with the help of two case studies, morphological levelling in Afrikaans and the erosion of null subjects in Afro-Peruvian Spanish; the model is found to be broadly in agreement with the historical development in both cases.
【2】 Evaluating the application of NLP tools in mainstream participatory budgeting processes in Scotland 标题：评估NLP工具在苏格兰主流参与式预算编制过程中的应用 链接：https://arxiv.org/abs/2111.11766
作者：Jonathan Davies,Miguel Arana-Catania,Rob Procter,Felix-Anselm van Lier,Yulan He 机构：Computer Science, Warwick University, Coventry, UK & Alan Turing Institute for Data Science and AI 备注：7 pages, presented at the 14th International Conference on Theory and Practice of Electronic Governance 2021. arXiv admin note: text overlap with arXiv:2109.09517 摘要：近年来，苏格兰的参与式预算编制（PB）已从少数几个社区主导的过程发展为一项由地方和国家政府支持的运动。苏格兰政府和《苏格兰地方当局公约》（COSLA）之间达成协议，规定至少1%的地方当局预算将受到PB的约束，这一点可以概括为一个缩影。这篇正在进行的研究论文探讨了在苏格兰32个地方当局中“扩大”或“主流化”所带来的挑战。主要目标是评估地方当局对数字平台CONSOR的使用情况，该平台应用自然语言处理（NLP）来应对这些挑战。本项目采用定性纵向设计，包括访谈、PB过程观察和数字平台数据分析。主题分析用于捕捉出现的主要问题和主题。纵向分析，然后探讨如何随着时间的推移这些演变。32个现场研究站点的潜力为探索离散的政治和社会背景提供了一个独特的机会，这些政治和社会背景具体化，并允许深入探讨可能存在的挑战和问题，而更广泛的跨部门研究将错过这一点。初步结果表明，可以使用NLP技术解决扩大规模带来的问题和挑战，在以前的受控基于用例的评估中，NLP技术已证明能够提高公民参与的有效性。 摘要：In recent years participatory budgeting (PB) in Scotland has grown from a handful of community-led processes to a movement supported by local and national government. This is epitomized by an agreement between the Scottish Government and the Convention of Scottish Local Authorities (COSLA) that at least 1% of local authority budgets will be subject to PB. This ongoing research paper explores the challenges that emerge from this 'scaling up' or 'mainstreaming' across the 32 local authorities that make up Scotland. The main objective is to evaluate local authority use of the digital platform Consul, which applies Natural Language Processing (NLP) to address these challenges. This project adopts a qualitative longitudinal design with interviews, observations of PB processes, and analysis of the digital platform data. Thematic analysis is employed to capture the major issues and themes which emerge. Longitudinal analysis then explores how these evolve over time. The potential for 32 live study sites provides a unique opportunity to explore discrete political and social contexts which materialize and allow for a deeper dive into the challenges and issues that may exist, something a wider cross-sectional study would miss. Initial results show that issues and challenges which come from scaling up may be tackled using NLP technology which, in a previous controlled use case-based evaluation, has shown to improve the effectiveness of citizen participation.
机器翻译，仅供参考
