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

文本的自动理解是一种能力，它将促进广泛的语言技术，包括信息提取、问题回答、观点分析和语言之间的翻译。多年来，这种技术在情报和国防领域一直很受欢迎，现在它们构成了许多商业信息管理工具的基础。这个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形式表示来理解它们，这是一种称为语义解析的技术。要在上面列出的技术中使用语义解析，它需要克服覆盖面广的根本挑战，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应对这一挑战，该方法以极大的降低成本收集经过语义注释的大型数据存储库；然后使用这些方法来训练更准确、覆盖面更广的解析模型。该项目的结果包括开放源码实现，前所未有的高质量标注语料库，以及可重用的分布式语义表示，供自然语言处理研究人员和实践者社区使用。只有同时关注具有语义意义的注释的新的大规模数据源和新的学习算法来归纳具有充分利用这些数据的表示能力的模型，才能实现广泛覆盖的语义分析的目标。对于可扩展的数据收集，该项目引入了依赖于两个关键补充见解的新技术：(1)任何理解文本的读者都可以回答有关它的问题，以及(2)可以构建其答案探测需要恢复的语义的任何方面的问题。这些观察结果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少语义注释的负担。这种QA风格的注释可以针对任何语言的任何文本进行，只要以本族语为母语，就可以绕过当前定义详细注释标准的大量工作。它还允许在比以往任何时候都更大的规模和更多样化的文本类型上收集新的数据集。此外，该项目开发了新的表示学习技术，将广泛的语义注释风格联系在一起，包括新的众包风格，在多任务学习设置中。连续表示(例如，单词类型)提供了一种强大的方式来允许在大词汇表上共享统计强度，其许多元素被稀疏地观察到。虽然过去的工作强调学习单词嵌入，但这个项目使用了一个共享的连续空间(“框架空间”)，可以捕获谓词论元(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他们被训练执行的任务，使用多个相关任务可以通过跨任务特定表征、跨语义词典元素、甚至跨语言共享统计强度，从而对所有这些任务产生好处。

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.