RI: Small: Learning to Retrieve Structured Information for Summarization and Translation of Unstructured Text

计算机越来越擅长生成自然语言，从完全不受约束（讲述随机故事）到高度受约束（将文本从一种语言翻译为另一种语言）。在更受约束的生成任务中，如翻译和摘要，现状是计算机主要（如果不是唯一的话）在示例输入输出对上进行训练，这可能导致听起来自然但不正确的输出。例如，新闻摘要可以很容易地，但错误地，将恐怖袭击中受害者的名字替换为他或她配偶的名字。相比之下，当人类学习翻译和总结时，示例输入-输出对只占我们“训练数据”的一小部分;我们还利用了大量我们学到的或可以在来源中查找的背景知识。该项目正在构建自动翻译和摘要系统，这些系统使用知识源来提高忠实性和事实正确性，提高此类系统的可用性，这些系统已经广泛用于信息访问。与许多以前试图将知识硬塞进数据的方法（例如，通过将字典定义作为错误的并行语句插入到训练数据中）或插入到模型中（例如，通过尝试改进单词嵌入），该项目的方法是将知识直接提供给生成系统。它侧重于将表格数据添加到摘要中，将字典数据（可以认为是一种表格）添加到翻译中，并将知识图添加到摘要和翻译中。该方法有三个阶段，反映了许多问答和对话系统中类似的设置。首先，该项目正在开发新的方法来学习如何从这些来源检索有用的信息。第二，检索到的知识可用于生成系统直接集成到系统的输入使用图形结构的表示。最后，图形到文本转换器的新扩展从这些增强的输入生成文本。该项目还在调查生成翻译文本的系统，这些文本增加了来自其知识来源的信息，这可能通过帮助以传统MT无法实现的方式弥合国家和文化障碍来改善信息获取。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估而被认为值得支持。

Computers are becoming ever more adept at generating natural language, in settings that range from totally unconstrained (tell a random story) to highly constrained (translate a text from one language to another). In more constrained generation tasks, like translation and summarization, the status quo is for computers to be trained primarily, if not exclusively, on example input-output pairs, which can lead to natural-sounding but incorrect outputs. For example, a news summarizer could easily, but erroneously, replace the name of a victim in a terror attack with the name of his or her spouse. By contrast, when humans learn to translate and summarize, example input-output pairs make up only a small fraction of our "training data"; we also draw on a vast amount of background knowledge that we've either learned or can look up in sources. This project is building automatic translation and summarization systems that use knowledge sources to improve faithfulness and factual correctness, increasing the usability of such systems, which are already widely used for information access.In contrast to many previous approaches that try to shoehorn knowledge into the data (e.g., by inserting dictionary definitions into the training data as ersatz parallel sentences) or into the model (e.g., by trying to improve word embeddings), this project's approach is to make knowledge available to the generation system directly. It focuses on adding table data to summarization and dictionary data (which can be thought of as a kind of table) to translation, and on adding knowledge graphs to both summarization and translation. The approach has three stages, which mirror similar setups in many question-answering and dialogue systems. First, the project is developing novel methods for learning how to retrieve useful information from these sources. Second, retrieved knowledge is made available to the generation system by directly integrating it into the system's input using a graph-structured representation. Finally, novel extensions of graph-to-text transformers generate text from these augmented inputs. The project is also investigating systems that generate translated text augmented with information from their knowledge sources, which may improve information access by helping to bridge national and cultural barriers in ways that conventional MT has not been able to.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.