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NSF-BSF: RI: Small: Collaborative Research: Modeling Crosslinguistic Influences Between Language Varieties

NSF-BSF：RI：小型：协作研究：模拟语言品种之间的跨语言影响

基本信息

批准号：
1812327
负责人：
Yulia Tsvetkov
金额：
$ 16.6万
依托单位：
Carnegie-Mellon University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1812327&HistoricalAwards=false
关键词：
NSF BSF RI Small Collaborative

项目摘要

Most people in the world today are multilingual. Though multilingualism is a gradual phenomenon, previous research has primarily examined text from second language learners who have not yet achieved fluency. This project focuses on text produced by nonnative but highly fluent speakers. Fluent but nonnative language differs subtly from native, monolingual language in the frequencies of certain concepts, constructions, and collocations. This raises the possibility that language technologies -- typically trained on "standard" native language -- are systematically biased in ways that render them less useful for the majority of users. This project will develop methods to examine large datasets of fluent nonnative language to detect the subtle influences of the native language and deliver natural language processing (NLP) tools for these language varieties. Its methods will be applicable beyond the populations in this study, including NLP-based measurement for social science and research seeking to better understand cognition in the bilingual mind. Native language identification will enable potential applications in language learning, cybersecurity, geolocation, personalization, and more. The project will openly share implementations and data, and will include educational activities that bring research into education.This project will advance natural language processing techniques to shed light on the differences in language use by fluent speakers with varying linguistic backgrounds: native speakers, highly fluent nonnative speakers, and translators when translating from another language into English. It is known that classifiers can be trained to discriminate with high accuracy among these populations, even though humans have difficulty telling them apart. This project will focus on semantic phenomena, which can confound even fluent nonnative speakers. If current NLP models are biased toward native language, then they may not support accurate measurement in nonnative text; the project will develop new techniques to mitigate this bias. This project will deliver a range of new models for native language identification, new measurement models and multi-variety models for language-variety-aware NLP tools, new semantic annotations in several Englishes, and a study on nonnative annotation. These novel methods for studying variation within a language and building such variation into our NLP systems will lead to unprecedented flexibility in computational models of natural language semantics.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.

今天世界上大多数人都是多语言的。虽然多语现象是一个渐进的现象，以前的研究主要是从尚未达到流利的第二语言学习者的文本。这个项目的重点是由非母语，但非常流利的发言者产生的文本。流利但非母语的语言在某些概念、结构和搭配的频率上与母语、单语语言有微妙的不同。这就提出了一种可能性，即语言技术-通常是在“标准”母语上训练的-系统地存在偏见，使它们对大多数用户不那么有用。该项目将开发方法来检查流利的非母语的大型数据集，以检测母语的微妙影响，并为这些语言种类提供自然语言处理（NLP）工具。它的方法将适用于本研究中的人群之外，包括基于NLP的社会科学测量和旨在更好地理解双语思维认知的研究。母语识别将使语言学习，网络安全，地理定位，个性化等方面的潜在应用成为可能。该项目将开放共享实施和数据，并将包括将研究引入教育的教育活动。该项目将推进自然语言处理技术，以揭示不同语言背景的流利使用者在语言使用方面的差异：母语使用者，高度流利的非母语使用者以及从另一种语言翻译成英语时的翻译。众所周知，分类器可以被训练成在这些人群中以高精度进行区分，即使人类很难区分它们。这个项目将集中在语义现象，它可以混淆甚至流利的非母语人士。如果当前的NLP模型偏向于母语，那么它们可能不支持非母语文本的准确测量;该项目将开发新技术来减轻这种偏见。该项目将提供一系列用于母语识别的新模型，用于语言多样性感知NLP工具的新测量模型和多多样性模型，几种英语的新语义注释以及对非母语注释的研究。这些研究语言内部变异的新方法，并将这种变异构建到我们的NLP系统中，将为自然语言语义的计算模型带来前所未有的灵活性。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

CMU-01 at the SIGMORPHON 2019 Shared Task on Crosslinguality and Context in Morphology

DOI：
10.18653/v1/w19-4208
发表时间：
2019-07
期刊：
ArXiv
影响因子：
0
作者：
Aditi Chaudhary;Elizabeth Salesky;G. Bhat;David R. Mortensen;J. Carbonell;Yulia Tsvetkov
通讯作者：
Aditi Chaudhary;Elizabeth Salesky;G. Bhat;David R. Mortensen;J. Carbonell;Yulia Tsvetkov

Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories

情境情感分析：网络人物描绘案例研究

DOI：
发表时间：
2019
期刊：
THE 13TH INTERNATIONAL AAAI CONFERENCE ON WEB AND SOCIAL MEDIA (ICWSM-2019
影响因子：
0
作者：
Field, Anjalie;Bhat, Gayatri;Tsvetkov, Yulia
通讯作者：
Tsvetkov, Yulia

Entity-Centric Contextual Affective Analysis

DOI：
10.18653/v1/p19-1243
发表时间：
2019-06
期刊：
ArXiv
影响因子：
0
作者：
Anjalie Field;Yulia Tsvetkov
通讯作者：
Anjalie Field;Yulia Tsvetkov

Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings

黑人之于罪犯就像白人之于警察：检测和消除词嵌入中的多类偏差

DOI：
发表时间：
2019
期刊：
2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL
影响因子：
0
作者：
Manzini, Thomas;Lim, Yao Chong;Tsvetkov, Yulia;Black, Alan W
通讯作者：
Black, Alan W

Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

俄罗斯新闻的框架和议程设置：复杂政治策略的计算分析

DOI：
发表时间：
2018
期刊：
2018 Conference on Empirical Methods in Natural Language Processing (EMNLP
影响因子：
0
作者：
Field, Anjalie;Kliger, Doron;Wintner, Shuly;Pan, Jennifer;Jurafsky, Dan;Tsvetkov, Yulia
通讯作者：
Tsvetkov, Yulia