RI: Small: Fast and Accurate Natural Language Parsing and Generation by Marrying Deep Learning with Dynamic Programming

RI：小型：将深度学习与动态规划相结合，快速准确地进行自然语言解析和生成

• 批准号: 1817231
• 负责人: Liang Huang
• 金额: $ 40万
• 依托单位: Oregon State 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 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817231&HistoricalAwards=false
• 关键词: RI; Small; Fast; Accurate; Natural

This grant aims to improve automatic understanding, generation, and translation of natural language by machines. Automated natural language processing has already changed the way we interact with digital assistants such as Amazon Echo and Apple Siri on smartphones and other devices. Automated machine translation reduces information barriers on the Web, where most information is inaccessible to most users because it is in a language they do not understand. Today's natural language systems, however, are limited to short exchanges and often make errors. These limitations are due to need for the systems to respond very quickly: current methods for more accurate understanding and generation take too long. This project will overcome this problem by developing new, fast, principled algorithms for these tasks. This project also supports STEM education of underrepresented minorities (who do not speak English natively) by recruiting them in machine translation studies.This grant aims to construct fast (linear-time) and accurate natural language parsers and generators (including translators) that utilize the power of both deep learning (for accurate and automatic feature engineering) and dynamic programming (to speed up the search). This project focuses on Recurrent Neural Network-based models (RNNs) such as Long Short Term Memory (LSTMs). In particular, this project aims to (1) Develop linear-time dynamic programming-based neural parsers by using RNNs to summarize the input text and extend them to joint syntactic-discourse parsing and predictive parsing. (2) Develop approximate dynamic programming algorithms and principled beam search methods for text generation and machine translation systems that use RNN-based decoders to model output text. (3) Combine the above two directions with an innovative application of simultaneous translation using predictive parsing.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.

该补助金旨在提高机器对自然语言的自动理解，生成和翻译。自动化自然语言处理已经改变了我们在智能手机和其他设备上与Amazon Echo和Apple Siri等数字助理的交互方式。 自动机器翻译减少了网络上的信息障碍，大多数信息对大多数用户来说是不可访问的，因为它是在一种他们不理解的语言中。 然而，今天的自然语言系统仅限于简短的交流，而且经常出错。 这些限制是由于系统需要非常快速地响应：当前用于更准确理解和生成的方法花费太长时间。 该项目将通过为这些任务开发新的，快速的，有原则的算法来克服这个问题。 该项目还通过招募机器翻译研究来支持代表性不足的少数民族（母语不是英语）的STEM教育。该资助旨在构建快速（线性时间）和准确的自然语言解析器和生成器（包括翻译器），利用深度学习（用于准确和自动的特征工程）和动态编程（用于加速搜索）的力量。该项目的重点是基于递归神经网络的模型（RNN），如长短期记忆（LSTM）。具体而言，本项目旨在（1）通过使用RNN来总结输入文本，并将其扩展到联合句法-话语分析和预测分析，开发基于线性时间动态规划的神经分析器。 (2)为文本生成和机器翻译系统开发近似动态规划算法和原则性波束搜索方法，这些系统使用基于RNN的解码器对输出文本进行建模。 (3)联合收割机将上述两个方向与使用预测解析的同声翻译的创新应用相结合。该奖项反映了NSF的法定使命，并通过使用基金会的知识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

Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings

通过合并伪引用并减少重新排序来改进同声翻译

• DOI: 10.18653/v1/2021.emnlp-main.473
• 发表时间: 2021
• 期刊: Proceedings of EMNLP 2021
• 作者: Chen, Junkun;Zheng, Renjie;Kita, Atsuhito;Ma, Mingbo;Huang, Liang
• 通讯作者: Huang, Liang

Learning to Stop in Structured Prediction for Neural Machine Translation

• DOI: 10.18653/v1/n19-1187
• 发表时间: 2019-04
• 作者: Mingbo Ma;Renjie Zheng;Liang Huang

Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR

• DOI: 10.18653/v1/2021.findings-acl.406
• 发表时间: 2021-06
• 作者: Junkun Chen;Mingbo Ma;Renjie Zheng;Liang Huang

LinearFold: linear-time approximate RNA folding by 5'-to-3' dynamic programming and beam search

• DOI: 10.1093/bioinformatics/btz375
• 发表时间: 2019-07-15
• 期刊: BIOINFORMATICS
• 影响因子: 5.8
• 作者: Huang, Liang;Zhang, He;Mathews, David H.
• 通讯作者: Mathews, David H.

Opportunistic Decoding with Timely Correction for Simultaneous Translation

机会解码并及时纠正同声翻译

• DOI: 10.18653/v1/2020.acl-main.42
• 发表时间: 2020
• 期刊: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
• 作者: Zheng, Renjie;Ma, Mingbo;Zheng, Baigong;Liu, Kaibo;Huang, Liang
• 通讯作者: Huang, Liang