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，使用从语料库归纳的精确、双向语法来关闭句法分析和生成之间的循环，从而有效地消除其含义。该方法将先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范围，只有在必要时才进行歧义消除，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解歧义的释义，使得探索使用众包的意义相似性判断来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)从诸如C&amp；C解析器之类的解析器的依赖输出中归纳OpenCCG语法，(2)使用OpenCCG生成显式地旨在避免可能的干扰解释的释义，(3)收集原始句子与其最可能的解释的释义之间的意义相似性判断，以及(4)使用收集的判断对解析器进行重新培训。为了在评估这一方法的同时进行推广，该项目除了使用亚马逊的机械土耳其语言外，还包括在俄亥俄州立大学COSI科学博物馆的语言研究舱进行数据收集和实验。通过关闭口译和生成之间的循环，该项目有望极大地提高使用众包来使自然语言处理工具适应新领域的前景。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的知识，为参加COSI的儿童提供一个鼓舞人心的科学实践范例。

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.