RI: Medium: Broad-Coverage Semantic Parsing: Linguistic Representation Learning from Crowd-Scale Data

文本的自动理解是一种将推进广泛语言技术的能力，包括信息提取、问答、意见分析和语言之间的翻译。 这些技术在情报和国防界的需求已经持续了很多年，它们现在是许多商业信息管理工具的基础。 该项目开发了强大的算法，通过将自然语言表达映射到其含义的正式表示来理解自然语言表达，这种技术称为语义分析。 要在上面列出的技术中使用语义解析，它需要克服广泛覆盖的基本挑战，即处理多种语言的任何文本输入的能力。 该项目通过创建新的方法来满足这一挑战，这些方法用于以大大降低的成本收集语义注释数据的大型存储库;然后使用这些方法来训练更准确的广泛覆盖的解析模型。 该项目的成果包括开源实现、规模空前的高质量注释语料库，以及可供自然语言处理研究人员和实践者社区使用的可重用分布式语义表示。广泛覆盖的语义解析的目标只能通过同时关注新的，大规模的数据源与语义上有意义的注释和新的学习算法，用于诱导模型的代表性能力，充分利用这些数据来实现。 对于可扩展的数据收集，该项目引入了依赖于两个关键互补见解的新技术：（1）任何理解文本的读者都可以回答有关文本的问题，（2）可以构建问题，其答案可以探索需要恢复的语义的任何方面。 这些观察允许设计新的数据收集技术，通过提供关于文本的简单问题和答案来减轻语义注释的负担。 这种QA风格的注释可以针对任何语言的任何文本完成，只针对母语人士，绕过了目前定义详细注释标准的重要工作。 它还允许在更大的规模上收集新的数据集，并且比以往任何时候都更多样化的文本类型。 此外，该项目还开发了新的表示学习技术，将各种语义注释风格（包括新的众包风格）结合在一起，用于多任务学习设置。 连续表示（例如，提供了一种强大的方法来允许在大型词汇表中共享统计强度，其中许多元素很少被观察到。 虽然过去的工作强调学习单词嵌入，但这个项目采用了一个共享的连续空间（“framespace”），可以捕获谓词-参数（和逻辑）语义中使用的抽象框架和角色。 这些表征的有用性取决于它们被训练来执行的任务，并且使用多个相关的任务可以通过在特定于任务的表征之间、在语义词典的元素之间、甚至在语言之间共享统计强度来使所有这些表征都受益。

Automated understanding of text is a capability that will advance a wide range of language technologies, including information extraction, question answering, opinion analysis, and translation between languages. Such technologies have been in demand in the intelligence and defense communities for many years, and they now underlie many commercially available information-management tools. This project develops robust algorithms that understand natural language expressions by mapping them to formal representations of their meaning, a technique known as semantic parsing. For semantic parsing to be employed in technologies like those listed above, it needs to overcome the fundamental challenge of broad coverage, the ability to handle any text input, in multiple languages. This project meets this challenge by creating new methods for gathering large repositories of semantically annotated data at greatly reduced cost; these are then used to train much more accurate broad-coverage parsing models. The results of this project include open-source implementations, high-quality annotated corpora on an unprecedented scale, and reusable distributed semantic representations for use by the community of natural language processing researchers and practitioners. The goal of broad-coverage semantic parsing can only be achieved by simultaneously focusing on new, large scale sources of data with semantically meaningful annotations and new learning algorithms for inducing models with the representational capacity to make full use of such data. For scalable data collection, this project introduces new techniques that rely on two key complementary insights: (1) any reader who understands a text can answer questions about it, and (2) questions can be constructed whose answers probe any aspect of semantics that need to be recovered. These observations allow designing new data collection techniques that reduce the burden of semantic annotation by providing simple questions and answers about texts. This QA-style annotation can be done for any text in any language, given only native speakers, bypassing the significant effort that currently goes into defining detailed annotation standards. It also allows gathering new datasets on a much larger scale, and for more diverse text types, than ever before. In addition, the project develops new representation learning techniques that tie together a wide range of semantic annotation styles, including the new crowdsourced ones, in a multitask learning setup. Continuous representations (e.g., of word types) provide a powerful way to allow sharing of statistical strength across a large vocabulary, many of whose elements are sparsely observed. While past work has emphasized learning word embeddings, this project employs a shared continuous space ("framespace") that can capture abstract frames and roles used in predicate-argument (and logical) semantics. The usefulness of these representations depends on the tasks they are trained to perform, and using multiple related tasks can lead to benefits on all of them, by sharing of statistical strength across task-specific representations, across elements of the semantic lexicon, and even across languages.