RI: Small: Closing the Loop: Inducing High-Precision Grammars for Generating Disambiguating Paraphrases

RI：小：闭环：引入高精度语法来生成消除歧义的释义

• 批准号: 1319318
• 负责人: Michael White
• 金额: $ 30万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319318&HistoricalAwards=false
• 关键词: RI; Small; Closing; Loop; Inducing

This project investigates trainable methods of paraphrasing natural language sentences to effectively disambiguate their meaning, using precise, bidirectional grammars induced from corpora to "close the loop"' between parsing and generation. The approach generalizes previous work on probabilistically avoiding ambiguity in natural language generation to a broad coverage setting, disambiguating only as necessary in order to better balance clarity and readability. Generating disambiguating paraphrases in a broad coverage setting makes it possible to explore ways of adapting parsers to new domains using crowd-sourced judgments of meaning similarity. Accordingly, the project explores methods of (1) inducing OpenCCG grammars from the dependency output of parsers such as the C&C parser, (2) generating paraphrases with OpenCCG that explicitly aim to avoid likely distractor interpretations, (3) collecting meaning similarity judgments between the original sentence and paraphrases of its most likely interpretations, and (4) retraining the parser using the collected judgments. To evaluate the approach while also conducting outreach, the project involves data collection and experimentation at Ohio State's language research pod at the COSI science museum, in addition to the use of Amazon's Mechanical Turk.By closing the loop between interpretation and generation, the project promises to dramatically enhance the prospects for using crowd-sourcing to adapt natural language processing tools to new domains. The project will also enable international collaborations with the University of Sydney, and help to educate the public about language science and technology, providing an inspirational example of science in action to the children who attend COSI.

本项目研究了可训练的方法，解释自然语言句子，有效地消除其含义，使用精确的，双向语法从语料库诱导的“闭环”之间的解析和生成。 该方法概括了以前的工作概率避免歧义的自然语言生成到一个广泛的覆盖面设置，消除歧义，只有在必要时，以更好地平衡清晰度和可读性。在广泛的覆盖范围内生成消歧释义，可以探索使用众包意义相似性判断使解析器适应新领域的方法。 因此，该项目探索的方法（1）诱导OpenCCG语法从依赖输出的解析器，如C C解析器，（2）生成释义与OpenCCG明确旨在避免可能的干扰解释，（3）收集之间的意义相似性判断的原始句子和释义的最可能的解释，（4）重新训练的分析器使用收集的判断。为了在进行推广的同时对该方法进行评估，该项目除了使用亚马逊的土耳其机器人（Mechanical Turk）外，还包括在COSI科学博物馆的俄亥俄州语言研究中心进行数据收集和实验。通过关闭解释和生成之间的循环，该项目有望大大提高使用众包使自然语言处理工具适应新领域的前景。 该项目还将实现与悉尼大学的国际合作，并帮助教育公众有关语言科学和技术，为参加COSI的儿童提供科学行动的鼓舞人心的例子。