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

RI：小型：用于句子理解和生成的缓存转换系统

• 批准号: 1813823
• 负责人: Daniel Gildea
• 金额: $ 40万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1813823&HistoricalAwards=false
• 关键词: RI; Small; Cache; transition; systems

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.

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。 此类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上运行的算法，并将允许统计自然处理技术更好地表示语义结构。 这些进步可以改进从文本中提取信息、在英语和中文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。 提高语言理解能力有助于访问网络上非结构化文本以及报纸和扫描书籍数据库中的大量信息。 语言之间翻译的改进增加了贸易机会以及国家和文化之间信息传播的机会。自然语言句子含义的基于图形的表示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生成响应。 然而，尽管句法分析最近取得了进展，但将句子自动解释为此类结构仍然是一项非常困难的任务。 该项目将开发用于从语义图解析和生成文本的算法，重点关注抽象含义表示或 AMR，尽管这些技术可推广到其他表示。 现有的 AMR 解析任务统计系统通常使用 ad-hoc 算法方法；要推进最先进的技术，就需要全新的算法。 该项目基于一种新的转换系统，称为缓存转换系统，专为解析图形结构的任务而定制。 初步实验表明，该系统与真实的语义图数据集非常匹配，因为它能够生成绝大多数观察到的图，同时简化了每一步预测下一个转换的机器学习问题。 该项目旨在通过开发和训练转换系统的神经版本来从输入字符串预测语义图，从而推进语义解析和生成的最新技术。 在最后一年，该项目将把解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

Ordered Tree Decomposition for HRG Rule Extraction

HRG 规则提取的有序树分解

• DOI: 10.1162/coli_a_00350
• 发表时间: 2019
• 期刊: Computational Linguistics
• 影响因子: 9.3
• 作者: Gildea, Daniel;Satta, Giorgio;Peng, Xiaochang
• 通讯作者: Peng, Xiaochang

SEMBLEU: A Robust Metric for AMR Parsing Evaluation

SEMBLEU：AMR 解析评估的稳健指标

• 发表时间: 2019
• 期刊: Association for Computational Linguistics (ACL
• 作者: Song, Linfeng;Gildea, Daniel
• 通讯作者: Gildea, Daniel

Efficient Outside Computation

高效的外部计算

• DOI: 10.1162/coli_a_00386
• 发表时间: 2021
• 期刊: Computational Linguistics
• 影响因子: 9.3
• 作者: Gildea, Daniel

Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation

• DOI: 10.18653/v1/2022.acl-short.80
• 发表时间: 2022
• 作者: Lisa Jin;D. Gildea

Generalized Shortest-Paths Encoders for AMR-to-Text Generation

• DOI: 10.18653/v1/2020.coling-main.181
• 发表时间: 2020-12
• 作者: Lisa Jin;D. Gildea