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

该项目研究了可训练的自然语言句子释义方法，以有效地消除其含义的歧义，使用从语料库中导出的精确双向语法来“关闭”解析和生成之间的“循环”。该方法将以前在自然语言生成中概率避免歧义的工作推广到广泛的覆盖设置，仅在必要时消除歧义，以便更好地平衡清晰度和可读性。在广泛的覆盖范围内生成消除歧义的释义，使得探索使用群体来源的意义相似性判断使解析器适应新领域的方法成为可能。因此，该项目探索了以下方法：(1)从解析器（如C&amp；C解析器）的依赖输出中引入OpenCCG语法；(2)使用OpenCCG生成释义，明确旨在避免可能的干扰解释；(3)收集原始句子与其最可能解释的释义之间的意义相似性判断；(4)使用收集到的判断重新训练解析器。为了评估这种方法，同时进行推广，除了使用亚马逊的土耳其机器人外，该项目还包括在COSI科学博物馆的俄亥俄州立大学语言研究舱收集数据和进行实验。通过关闭解释和生成之间的循环，该项目有望极大地提高使用众包的前景，使自然语言处理工具适应新的领域。该项目还将促进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。

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.