Studying the Difference Between Natural and Programming Language Corpora
Department of Computer Science, University of California, Davis（加利福尼亚大学戴维斯分校计算机科学系）；Department of Linguistics, University of California, Davis（加利福尼亚大学戴维斯分校语言学系）
Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora. But why? Does the difference simply arise from the syntactic limitations of programming languages? Or does it arise from the differences in authoring decisions made by the writers of these natural and programming language texts? We conjecture that the differences are not entirely due to syntax, but also from the fact that reading and writing code is un-natural for humans, and requires substantial mental effort; so, people prefer to write code in ways that are familiar to both reader and writer. To support this argument, we present results from two sets of studies: 1) a first set aimed at attenuating the effects of syntax, and 2) a second, aimed at measuring repetitiveness of text written in other settings (e.g. second language, technical/specialized jargon), which are also effortful to write. We find find that this repetition in source code is not entirely the result of grammar constraints, and thus some repetition must result from human choice. While the evidence we find of similar repetitive behavior in technical and learner corpora does not conclusively show that such language is used by humans to mitigate difficulty, it is consistent with that theory.
Studying the History of the Arabic Language: Language Technology and a Large-Scale Historical Corpus
MIT Computer Science and Artificial Intelligence Laboratory（麻省理工学院计算机科学与人工智能实验室）；University of Rhode Island（罗德岛大学）；Qatar Computing Research Institute, HBKU（哈马德·本·哈利法大学卡塔尔计算研究所）；Bar-Ilan University（巴伊兰大学）；Dicta: The Israel Center for Text Analysis（以色列文本分析中心（Dicta））；University of Vienna（维也纳大学）
DEBISS: a Corpus of Individual, Semi-structured and Spoken Debates
Federal University of Campina Grande (UFCG) - System（坎皮纳格雷联邦大学（UFCG）- 系统）；Computing Campina Grande, Brazil Federal Institute of Paraíba (IFPB) Monteiro, Brazil（坎皮纳格雷计算，巴西帕拉州联邦理工学院（IFPB）蒙特伊罗，巴西）
Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus
Department of Physics and Chemistry, University of Palermo（帕尔米洛大学物理与化学系）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), UIB-CSIC（跨学科物理与复杂系统研究所（IFISC）， UIB-CSIC）
Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Sea AI Lab（Sea AI实验室）；Generative AI Research Lab (GAIR)（生成式人工智能研究实验室（GAIR））
Code and Named Entity Recognition in StackOverflow
The Ohio State University（俄亥俄州立大学）
随着大量的编程文本在互联网上变得容易获得，一起学习自然语言和计算机代码的兴趣越来越大。例如，StackOverflow目前有超过1500万个编程相关的问题，由850万用户编写。与此同时，仍然缺乏基本的NLP技术来识别出现在自然语言句子中的代码令牌或软件相关的命名实体。在本文中，我们介绍了一个新的命名实体识别（NER）语料库的计算机编程领域，由15,372个句子与20个细粒度的实体类型注释。我们在来自StackOverflow的1.52亿个句子上训练了域内BERT表示（BERTOverflow），这导致了比现成的BERT绝对增加了+10的F-1分数。我们还提出了SoftNER模型，该模型在StackOverflow数据上实现了代码和命名实体识别的总体79.10 F $_1$得分。我们的SoftNER模型采用了一个上下文无关的代码令牌分类器与语料库级别的功能，以改善基于BERT的标记模型。我们的代码和数据可在https://github.com/jeniyat/StackOverflowNER/上获得
Native Design Bias: Studying the Impact of English Nativeness on Language Model Performance
Research Centre for Information Systems Engineering (LIRIS), KU Leuven（信息系统工程研究中心（LIRIS），库勒文大学）；IESEG School of Management（IESEG管理学院）；Department of Decision Analytics and Risk, University of Southampton（决策分析与风险系，南安普顿大学）
CodePivot: Bootstrapping Multilingual Transpilation in LLMs via Reinforcement Learning without Parallel Corpora
The Hong Kong University of Science and Technology（香港科学与技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Monash University（莫纳什大学）；The Chinese University of Hong Kong（香港中文大学）
Bridging the Knowledge Void: Inference-time Acquisition of Unfamiliar Programming Languages for Coding Tasks
State Key Laboratory for Novel Software Technology, Nanjing University, China（新型软件技术国家重点实验室，南京大学，中国）；National Institute of Healthcare Data Science, Nanjing University, China（健康数据科学国家研究院，南京大学，中国）
