CAREER: Interactive Training of Semantic Parsers via Paraphrasing

随着Siri等虚拟助手的日益普及，对深度和强大的语言理解的需求再次出现。统计语义解析是解决这一需求的一个很有前途的范例。建立统计语义解析器的关键障碍是获得足够的训练数据。这个CAREER项目旨在开发一个新的交互式框架来构建一个语义解析器，在这个框架中，系统就像一个讲英语的外国人，要求用户将计算机已经理解的话语解释成计算机不理解的话语。该框架在教育领域开辟了有趣的应用。其中一个应用是双向辅导系统，该系统向学生提出问题。学生必须既回答问题又解释问题，从而既练习了课程材料，又为系统提供了训练数据。自然语言是一个普遍的切入点，它可以增加参与度，促进多样性。高质量的语义解析器可以极大地改善人类与计算机交互的方式。从长远来看，这项工作也会对自然语言处理系统的构建方式产生重大影响。目前，流行的范例在很大程度上是训练和部署的范例，而如果部署的系统能够实时学习，则有更多改进和个性化的机会。该项目开发了一个新的交互式框架，用于构建语义解析器，旨在获得给定领域的完整覆盖。关键思想是让系统选择逻辑形式，生成捕获其语义的探测话语，并要求用户将其解释为自然输入话语。在此过程中，系统学习语言变化和新的高级概念。然后使用这些数据来训练基于释义的语义解析模型。现有的释义模型要么是基于变换的，擅长捕捉语言的结构规律，要么是基于向量的，擅长捕捉软相似性。该项目开发了新的模型来捕捉这两者。该项目开发的框架从三个方面提高了自然语言处理和机器学习的水平。首先，该框架脱离了收集数据集和学习模型的经典范式；相反，一个交互系统将这两个步骤交织在一起。其次，该框架学习高级概念，这对自然语言理解至关重要，因为单词通常代表复杂的概念。最后，它通过在统一模型中捕获逻辑表示的刚性和连续表示的灵活性，解决了逻辑表示的刚性和连续表示的灵活性之间的经典紧张关系。

With the increase in popularity of virtual assistants such as Siri, there is a renewed demand for deep and robust language understanding. Statistical semantic parsing is a promising paradigm for addressing this demand. The key obstacle in building statistical semantic parsers is obtaining adequate training data. This CAREER project aims to develop a new interactive framework for building a semantic parser, where the system, acting like a foreign speaker of English, asks users to paraphrase utterances that the computer already understands into ones that the computer doesn't. The framework opens up intriguing applications in education. One such application is a bidirectional tutoring system, in which the system poses questions to the student. The student must both answer and paraphrase the question, thereby both practicing the course material and providing training data to the system. Natural language is a universal entry point, which can increase engagement and promote diversity. High-quality semantic parsers can drastically improve the way humans interact with computers. In the longer term, this work can also have a significant impact on the way natural language processing systems are built. Currently, the prevailing paradigm is very much a train-and-deploy one, whereas there are many more opportunities for improvement and personalization if deployed systems were to learn on-the-fly.This project develops a new interactive framework for building a semantic parser, which aims to obtain complete coverage in a given domain. The key idea is for the system to choose logical forms, generate probe utterances that capture their semantics, and ask users to paraphrase them into natural input utterances. In the process, the system learns about linguistic variation and novel high-level concepts. The data is then used to train a paraphrasing-based semantic parsing model. Existing paraphrasing models are either transformation-based, which excel at capturing structural regularities in language or are vector-based, which excel at capturing soft similarity. The project develops novel models to capture both. The framework developed in this project improves the state-of-the-art of natural language processing and machine learning in three ways. First, the framework departs from the classic paradigm of gathering a dataset and learning a model; instead, an interactive system interleaves the two steps. Second, the framework learns high-level concepts, which is crucial for natural language understanding, since words often represent complex concepts. Finally, it resolves a classic tension between the rigidity of logical representations and the flexibility of continuous representations by capturing both in a unified model.