RI: EAGER: Exploratory Research on Acquiring and Adapting Sentence Planning Resources for Generating with Discourse Combinatory Categorial Grammar

RI：EAGER：获取和调整句子规划资源以生成语篇组合范畴语法的探索性研究

• 批准号: 1143635
• 负责人: Michael White
• 金额: $ 14.99万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1143635&HistoricalAwards=false
• 关键词: RI; EAGER; Exploratory; Research; Acquiring

Natural Language Generation (NLG) systems aim to improve the accessibility and impact of information by turning data into coherent and fluent text or speech, automatically. Developing high-quality NLG systems, however, remains a difficult and costly undertaking, in large part because bridging the gap between content planning and surface realization---a task known as \textit{sentence planning}---continues to require extensive knowledge engineering.This Early Grant for Exploratory Research investigates ways of bridging this gap by employing machine learning together with Discourse Combinatory Categorial Grammar (DCCG). Using a restaurant recommendation application as a proof-of-concept, the project explores methods of (1) adapting previous work on acquiring lexicalized grammar entries for semantic parsing to learn mappings from domain-general semantic dependency representations to application-specific representations of messages; (2) extending the approach to learn rules for combining messages; (3) employing the acquired resources to map content plans to disjunctive logical forms (DLFs), which compactly specify the range of possible realizations of the selected content; and (4) improving the efficiency of realizing DLFs with OpenCCG through grammar specialization.The project will evaluate the success of these novel methods and assess the portability of the approach. By demonstrating methods for radically simplifying the construction of NLG systems, the project promises to transform the way NLG systems are built, from today's knowledge-intensive approach to one that relies primarily on assembling a parallel corpus of input-output pairs. Ultimately, it will facilitate the development of generation components in data-to-text systems as well as dialogue systems, including ones for the visually impaired.

自然语言生成（NLG）系统旨在通过自动将数据转换为连贯流畅的文本或语音来提高信息的可访问性和影响力。然而，开发高质量的NLG系统仍然是一项困难而昂贵的任务，这在很大程度上是因为弥合内容规划和表面实现之间的差距——一项被称为\textit{句子规划}的任务——仍然需要广泛的知识工程。这项探索性研究的早期资助研究了通过使用机器学习和话语组合范畴语法（DCCG）来弥合这一差距的方法。使用一个餐厅推荐应用程序作为概念验证，该项目探索了以下方法：(1)调整以前的工作，获取用于语义解析的词汇化语法条目，以学习从一般领域的语义依赖表示到特定于应用程序的消息表示的映射；(2)扩展了信息组合规则的学习方法；(3)利用获得的资源将内容计划映射到析取逻辑形式（dlf），该形式紧凑地指定了所选内容的可能实现范围；(4)通过语法专门化提高OpenCCG实现dlf的效率。该项目将评估这些新方法的成功，并评估方法的可移植性。通过演示从根本上简化NLG系统构建的方法，该项目有望改变NLG系统的构建方式，从今天的知识密集型方法转变为主要依赖于组装输入输出对的并行语料库的方法。最终，它将促进数据转文本系统和对话系统（包括视障人士对话系统）中生成组件的开发。