CI-ADDO-NEW: Collaborative Research: A Repository for Annotating Multilingual Code Switched Data

CI-ADDO-NEW：协作研究：用于注释多语言代码交换数据的存储库

• 批准号: 1205475
• 负责人: Thamar Solorio
• 金额: $ 36.43万
• 依托单位: University of Alabama at Birmingham
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1205475&HistoricalAwards=false
• 关键词: CI; ADDO; NEW; Collaborative; Research

Linguistic code switching (LCS) is the practice of switching back and forth between the shared languages of bilingual or multilingual speakers. This phenomenon is particularly prevalent in geographic regions with linguistic boundaries or where there are large immigrant groups. Various levels of language (phonological, morphological, syntactic, semantic and discourse-pragmatic) may be implicated in LCS in different language pairs and/or genres. Computational algorithms trained for a single language quickly break down when the input includes LCS. A major barrier to research on LCS in computational linguistics (CL) has been the lack of large, accurately annotated corpora of LCS data. In this project, a large repository of LCS data is collected and a large annotation infrastructure is developed. It is consistently annotated in different modalities (speech and text), at various levels of linguistic granularity, and across different language pairs reflecting different linguistic typologies (Standard Arabic and Dialectal Arabic, Arabic-English, Spanish-English, Chinese-English, Hindi-English). The focus of the effort is on intra-sentential LCS.This infrastructure and unified large LCS data resource is eagerly awaited by the CL research community, since annotated LCS data provides a natural test-bed for adaptive learning algorithms and the handling of diverse data sources, as well as a framework for genuine multilingual processing. It will also be of benefit to sociolinguistic and theoretical linguistic researchers, and provide a platform for collaborative interdisciplinary research. Finally, research on LCS helps overcome biases against multilingual speakers by demonstrating the creativity of such speakers in exploiting their verbal repertoires. Such a result is particularly important for K-12 education and testing policies in the USA with its diverse immigrant population.

语言代码转换（LCS）是在双语或多语言使用者的共享语言之间来回切换的做法。这种现象在有语言界限或有大量移民群体的地理区域尤其普遍。不同语言对和/或流派的 LCS 可能涉及不同级别的语言（语音、形态、句法、语义和话语语用）。当输入包含 LCS 时，针对单一语言训练的计算算法很快就会崩溃。计算语言学 (CL) 中 LCS 研究的一个主要障碍是缺乏大型、准确注释的 LCS 数据语料库。在该项目中，收集了大型 LCS 数据存储库并开发了大型注释基础设施。它以不同的方式（语音和文本）、不同的语言粒度级别以及反映不同语言类型的不同语言对（标准阿拉伯语和方言阿拉伯语、阿拉伯语-英语、西班牙语-英语、汉语-英语、印地语）进行一致注释。英语）。这项工作的重点是句内 LCS。这种基础设施和统一的大型 LCS 数据资源受到 CL 研究社区的热切期待，因为带注释的 LCS 数据为自适应学习算法和处理不同数据源提供了天然的测试平台，以及真正的多语言处理框架。它还将使社会语言学和理论语言学研究人员受益，并为跨学科合作研究提供平台。最后，LCS 研究通过展示多语言使用者在利用其言语技能方面的创造力，帮助克服对多语言使用者的偏见。这样的结果对于移民人口多元化的美国的 K-12 教育和考试政策尤为重要。