Language is highly structured, with syntactic and semantic structures, to some extent, agreed upon by speakers of the same language. With implicit or explicit awareness of such structures, humans can learn and use language efficiently and generalize to sentences that contain unseen words. Motivated by human language learning, in this dissertation, we consider a family of machine learning tasks that aim to learn language structures through grounding. We seek distant supervision from other data sources (i.e., grounds), including but not limited to other modalities (e.g., vision), execution results of programs, and other languages. We demonstrate the potential of this task formulation and advocate for its adoption through three schemes. In Part I, we consider learning syntactic parses through visual grounding. We propose the task of visually grounded grammar induction, present the first models to induce syntactic structures from visually grounded text and speech, and find that the visual grounding signals can help improve the parsing quality over language-only models. As a side contribution, we propose a novel evaluation metric that enables the evaluation of speech parsing without text or automatic speech recognition systems involved. In Part II, we propose two execution-aware methods to map sentences into corresponding semantic structures (i.e., programs), significantly improving compositional generalization and few-shot program synthesis. In Part III, we propose methods that learn language structures from annotations in other languages. Specifically, we propose a method that sets a new state of the art on cross-lingual word alignment. We then leverage the learned word alignments to improve the performance of zero-shot cross-lingual dependency parsing, by proposing a novel substructure-based projection method that preserves structural knowledge learned from the source language.

翻译：语言具有高度结构化的特征，其句法和语义结构在一定程度上被同一语言的使用者所共识。凭借对这种结构的隐式或显式认知，人类能够高效地学习并使用语言，并能泛化到包含未见词汇的句子。受人类语言学习的启发，本论文探讨了一系列旨在通过接地学习语言结构的机器学习任务。我们寻求来自其他数据源（即接地信号）的远程监督，这些数据源包括但不限于其他模态（如视觉）、程序执行结果以及其他语言。我们通过三种方案展示了该任务框架的潜力并倡导其采用。在第一部分中，我们考虑通过视觉接地学习句法解析。我们提出了视觉接地语法归纳任务，首次提出了从视觉接地的文本和语音中归纳句法结构的模型，并发现视觉接地信号有助于提升纯语言模型的解析质量。作为一项附带贡献，我们提出了一种新颖的评估指标，使得无需文本或自动语音识别系统即可评估语音解析。在第二部分中，我们提出了两种执行感知方法，将句子映射到相应的语义结构（即程序），显著提升了组合泛化能力和少样本程序合成性能。在第三部分中，我们提出了从其他语言的标注中学习语言结构的方法。具体而言，我们提出了一种在跨语言词对齐任务上达到最新技术水平的方法。随后，我们通过提出一种基于子结构的新型投影方法——该方法能保留从源语言学习到的结构知识——利用学习到的词对齐来提升零样本跨语言依存句法解析的性能。