RI: Small: Cache transition systems for sentence understanding and generation

基于图的语义表示允许计算机存储和处理自然语言文本中的信息。这样的图包含表示事件和实体的节点，以及表示关系的节点之间的边。该项目将开发直接在图形上操作的算法，并将允许统计自然处理技术更好地表示语义结构。这些进展可以改进从文本中提取信息、在英语和汉语等人类语言之间进行翻译以及在自然语言对话中与人类互动的系统。提高对语言的理解有助于获取网络上非结构化文本以及报纸和扫描图书数据库中的大量信息。改进的语言之间的翻译增加了国家和文化之间的贸易和信息传播的机会。自然语言句子的意义的基于图形的表示正被越来越多地用于推理任务，包括回答问题，合并来自不同来源的信息，以及在对话系统中产生响应。然而，尽管句法分析最近取得了进展，但将句子自动解释成这样的结构仍然是一项非常困难的任务。这个项目将开发用于解析到语义图并从语义图生成文本的算法，重点是抽象意义表示或AMR，尽管这些技术适用于其他表示法。用于AMR解析任务的现有统计系统通常使用自组织算法方法；从根本上讲，需要新的算法来提高技术水平。这个项目基于一个新的转换系统，称为缓存转换系统，专为解析成图结构的任务量身定做。初步实验表明，该系统能够很好地匹配语义图的真实数据集，能够生成绝大多数观察到的图，同时简化了预测每一步下一个转变的机器学习问题。这个项目旨在通过开发和训练一个神经版本的转换系统来预测输入字符串的语义图，从而提高语义分析和生成的技术水平。在项目的最后一年，该项目将把分析和生成方法应用于机器翻译任务，为神经机器翻译系统提供语义图和源语言字符串。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。

Graph-based semantic representations allow computers to store and process information in natural language text. Such graphs contain nodes representing events and entities, and edges between nodes representing relations. This project will develop algorithms that operate directly on graphs, and will allow statistical natural processing techniques to better represent semantic structures. These advances can improve systems that extract information from text, translate between human languages such as English and Chinese, and interact with humans in natural language dialog. Improved language understanding can help in accessing the enormous amount of information available in unstructured text on the web as well as in databases of newspapers and scanned books. Improved translation between languages increases opportunities for trade as well as for dissemination of information generally between nations and cultures.Graph-based representations of the meaning of natural language sentences are being used to an increasing degree for reasoning tasks including question answering, merging information from disparate sources, and generating responses in dialog systems. However, automatic interpretation of sentences into such structures remains a very difficult task, despite recent progress in syntactic parsing. This project will develop algorithms for parsing into and generating text from semantic graphs, focusing on Abstract Meaning Representation or AMR, although the techniques generalize to other representations. Existing statistical systems for the AMR parsing task generally use ad-hoc algorithmic approaches; fundamentally new algorithms are necessary to advance the state of the art. This project is based on a new transition system, called a cache transition system, tailored to the task of parsing into graph structures. Preliminary experiments show that the system is a good match to real datasets of semantic graphs, in that it is able to produce the vast majority of graphs observed while at the same time simplifying the machine learning problem of predicting the next transition at each step. This project aims to advance the state of the art in semantic parsing and generation by developing and training a neural version of the transition system to predict semantic graphs from input strings. In its final year, the project will apply the parsing and generation methods to the task of machine translation, providing semantic graphs along with source language strings to a neural machine translation system.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.