Collaborative Research: RI: Small: NL(V)P: Natural Language (Variety) Processing

合作研究：RI：小型：NL(V)P：自然语言（品种）处理

• 批准号: 2125466
• 负责人: Antonios Anastasopoulos
• 金额: $ 16.6万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2125466&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; Small; NL

No language is a monolith. Languages vary richly across countries, regions, social classes, and other factors. Despite recent advances in natural language processing (NLP) technology for translating between languages, answering questions, or engaging in simple conversations, current approaches have largely focused only on "standard" varieties of languages. By ignoring other varieties, treating them essentially as statistical noise, current technologies neglect the millions of people who speak these varieties. This project is creating ways to enable language technologies such as translation and question-answering systems, both to process and to generate fine-grained language varieties. The team will develop computational methods to automatically recognize features of different language varieties and then create approaches for integrating such linguistic information into the models powering language technologies. Additionally, the team will design methods to adapt models into varieties for which minimal training data may be available. The resulting suite of general methods will benefit diverse communities and less-privileged populations that speak underserved languages and varieties.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

没有一种语言是铁板一块。语言因国家、地区、社会阶层和其他因素的不同而有很大差异。尽管用于在语言之间进行翻译、回答问题或进行简单对话的自然语言处理(NLP)技术最近取得了进展，但目前的方法主要集中在语言的“标准”变体上。通过忽略其他变体，将它们本质上视为统计噪音，当前的技术忽视了数百万说这些变体的人。该项目正在创造各种方法，使翻译和问答系统等语言技术能够处理和生成细粒度的语言变体。该团队将开发计算方法，自动识别不同语言变体的特征，然后创建方法，将这些语言信息整合到为语言技术提供动力的模型中。此外，该团队将设计方法，使模型适应可获得最少训练数据的品种。由此产生的一套通用方法将使说服务不足的语言和各种语言的不同社区和较少特权的人口受益。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Dataset Geography: Mapping Language Data to Language Users

• DOI: 10.18653/v1/2022.acl-long.239
• 发表时间: 2021-12
• 作者: FAHIM FAISAL;Yinkai Wang;Antonios Anastasopoulos

SD-QA: Spoken Dialectal Question Answering for the Real World

SD-QA：现实世界的口语方言问答

• DOI: 10.18653/v1/2021.findings-emnlp.281
• 发表时间: 2021
• 期刊: Findings of the Association for Computational Linguistics: EMNLP 2021
• 作者: Faisal, Fahim;Keshava, Sharlina;Alam, Md Mahfuz;Anastasopoulos, Antonios
• 通讯作者: Anastasopoulos, Antonios

Machine Translation into Low-resource Language Varieties

• DOI: 10.18653/v1/2021.acl-short.16
• 发表时间: 2021-06
• 期刊: ArXiv
• 作者: Sachin Kumar;Antonios Anastasopoulos;S. Wintner;Yulia Tsvetkov

GMU Systems for the IWSLT 2023 Dialect and Low-resource Speech Translation Tasks

用于 IWSLT 2023 方言和低资源语音翻译任务的 GMU 系统

• DOI: 10.18653/v1/2023.iwslt-1.24
• 发表时间: 2023
• 期刊: Association for Computational Linguistics
• 作者: Mbuya, Jonathan;Anastasopoulos, Antonios
• 通讯作者: Anastasopoulos, Antonios

Evaluating the Morphosyntactic Well-formedness of Generated Texts

• DOI: 10.18653/v1/2021.emnlp-main.570
• 发表时间: 2021-03
• 期刊: ArXiv
• 作者: Adithya Pratapa;Antonios Anastasopoulos;Shruti Rijhwani;Aditi Chaudhary;David R. Mortensen;Graham Neubig;Yulia Tsvetkov