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

这项拨款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亚马逊回声(Amazon Echo)和苹果Siri等数字助理互动的方式。自动化机器翻译减少了网络上的信息障碍，大多数用户无法访问网络上的大多数信息，因为它们使用的是他们不理解的语言。然而，今天的自然语言系统仅限于短暂的交流，而且经常出错。这些限制是由于系统需要非常快速的响应：当前用于更准确地理解和生成的方法花费的时间太长。这个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。该项目还通过在机器翻译研究中招募代表不足的少数族裔(他们的母语不说英语)来支持STEM教育。这笔赠款旨在建立快速(线性时间)和准确的自然语言分析器和生成器(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(以加快搜索)的能力。该项目专注于基于循环神经网络的模型(RNN)，例如长期短期记忆(LSTM)。具体地说，本项目的目标是(1)开发基于线性时间动态规划的神经分析器，使用RNN对输入文本进行摘要，并将其扩展到句法-语篇联合分析和预测分析。(2)为使用基于RNN的解码器对输出文本建模的文本生成和机器翻译系统开发了近似动态规划算法和原理波束搜索方法。(3)将上述两个方向与使用预测分析的同声传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。

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.