PIRE: Investigation of Meaning Representations in Language Understanding for Machine Translation Systems

PIRE:机器翻译系统语言理解中的意义表示研究

基本信息

  • 批准号:
    0530118
  • 负责人:
  • 金额:
    $ 249.84万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2005
  • 资助国家:
    美国
  • 起止时间:
    2005-10-01 至 2015-09-30
  • 项目状态:
    已结题

项目摘要

----This Partnership for International Research and Education (PIRE) links senior and junior researchers from Johns Hopkins University and Brown University with counterparts from Charles University in the Czech Republic and Saarland University in Germany. The international team, led by Frederick Jelinek at Johns Hopkins, will investigate formal representations of linguistic meaning for use in speech recognition/reconstruction and machine translation (MT) systems. Their goal is to augment current speech recognition systems by applying a variety of formal models for deep syntactic/semantic representation so that output of their refined MT system becomes coherent, grammatical text.The projects complementary education component involves introducing participating U.S. graduate students to European-developed linguistic formalisms and training them to apply those formalisms to problems in natural language processing. Students will have language training in Czech or German and will spend at least one semester abroad where they will further their linguistic training in tectogrammatical representation at Charles University or head-driven phrase structure grammar at Saarland University. In the final stages of their Ph.D. program, each will return to the Czech Republic or Germany to work with European mentors on research that incorporates these state-of-the art language processing techniques. Results from the collaborative research, annual workshops and cross-training should advance the field of computational linguistics by integrating formal meaning representations and statistical methods for natural language processing so that modern computer resources can be exploited to more rapidly translate verbal communications from other languages into English. If successful, this work could revolutionize language modeling for automatic speech recognition so that even spontaneous speech may be translated into fluent, reconstructed text that efficiently captures the intended meaning of the original. This interdisciplinary PIRE in computational linguistics fulfills the program objective of advancing scientific knowledge by enabling experts in the United States and Europe to combine complementary talents and share research resources in areas of strong mutual interest and competence. Broader impacts include early career introduction of U.S. graduate students to an international professional network of leading linguists, computational theorists, and experts in human language technology.
- 国际研究和教育伙伴关系(PIRE)将约翰霍普金斯大学和布朗大学的高级和初级研究人员与捷克共和国查尔斯大学和德国萨尔兰大学的同行联系起来。 由约翰霍普金斯大学的Frederick Jelinek领导的国际团队将研究用于语音识别/重建和机器翻译(MT)系统的语言意义的正式表示。他们的目标是通过应用各种形式模型进行深层句法/语义表示来增强当前的语音识别系统,以便其精炼的MT系统的输出成为连贯的语法文本。该项目的补充教育部分包括向参与的美国研究生介绍欧洲开发的语言形式主义,并培训他们将这些形式主义应用于自然语言处理中的问题。 学生将在捷克语或德语的语言培训,并将在国外度过至少一个学期,他们将在查尔斯大学或萨尔兰大学的头驱动短语结构语法进一步他们的语言培训tectogramm表示。 在他们博士学位的最后阶段。计划,每个人将返回捷克共和国或德国与欧洲导师的研究,结合这些国家的最先进的语言处理技术。 从合作研究,年度研讨会和交叉培训的结果应推进计算语言学领域的整合正式意义表示和统计方法的自然语言处理,使现代计算机资源可以被利用,以更快地将口头交流从其他语言翻译成英语。 如果成功的话,这项工作可以彻底改变自动语音识别的语言建模,这样即使是自发的语音也可以被翻译成流畅的重建文本,有效地捕捉原始的意图。这个跨学科的计算语言学PIRE实现了通过使美国和欧洲的专家能够联合收割机互补人才和共享研究资源来推进科学知识的计划目标。 更广泛的影响包括美国研究生的早期职业介绍,以领先的语言学家,计算理论家和人类语言技术专家的国际专业网络。

项目成果

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Sanjeev Khudanpur其他文献

Getting more from automatic transcripts for semi-supervised language modeling
  • DOI:
    10.1016/j.csl.2015.08.007
  • 发表时间:
    2016-03-01
  • 期刊:
  • 影响因子:
  • 作者:
    Scott Novotney;Richard Schwartz;Sanjeev Khudanpur
  • 通讯作者:
    Sanjeev Khudanpur
A dilemma of ground truth in noisy speech separation and an approach to lessen the impact of imperfect training data
  • DOI:
    10.1016/j.csl.2022.101410
  • 发表时间:
    2023-01-01
  • 期刊:
  • 影响因子:
  • 作者:
    Matthew Maciejewski;Jing Shi;Shinji Watanabe;Sanjeev Khudanpur
  • 通讯作者:
    Sanjeev Khudanpur
Towards machines that know when they do not know: Summary of work done at 2014 Frederick Jelinek Memorial workshop
走向知道何时不知道的机器:2014 年 Frederick Jelinek 纪念研讨会所做工作总结
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hynek Hermansky;Lukas Burget;Jordan Cohen;Emmanuel Dupoux Naomi Feldman;John Godfrey;Sanjeev Khudanpur;Matthew Maciejewski;Sri Harish Mallidi;Anjali Menon;Tetsuji Ogawa;Vijayaditya Peddinti;Richard Rose;Richard Stern;Matthew Wiesner;Karel Ve
  • 通讯作者:
    Karel Ve

Sanjeev Khudanpur的其他文献

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{{ truncateString('Sanjeev Khudanpur', 18)}}的其他基金

CCRI: ENS: Next Generation Tools for Spoken Language Science & Technology
CCRI:ENS:下一代口语科学工具
  • 批准号:
    2120435
  • 财政年份:
    2021
  • 资助金额:
    $ 249.84万
  • 项目类别:
    Standard Grant
RI: Medium: Collaborative Research: Semi-Supervised Discriminative Training of Language Models
RI:媒介:协作研究:语言模型的半监督判别训练
  • 批准号:
    0963898
  • 财政年份:
    2010
  • 资助金额:
    $ 249.84万
  • 项目类别:
    Continuing Grant
Cross-Cutting Research Workshops on Intelligent Information Systems
智能信息系统跨领域研究研讨会
  • 批准号:
    1005411
  • 财政年份:
    2010
  • 资助金额:
    $ 249.84万
  • 项目类别:
    Continuing Grant
SGER: Self-Supervised Discriminative Training of Statistical Language Models
SGER:统计语言模型的自监督判别训练
  • 批准号:
    0840112
  • 财政年份:
    2008
  • 资助金额:
    $ 249.84万
  • 项目类别:
    Standard Grant
SGER: Pronunciation Modeling for Conversational Speech Recognition
SGER:会话语音识别的发音建模
  • 批准号:
    9714169
  • 财政年份:
    1997
  • 资助金额:
    $ 249.84万
  • 项目类别:
    Standard Grant

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