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.

本文提出了基于语言结构的机器学习任务，并通过三种方案展示了其潜力和可行性，包括通过视觉基现学习语法结构，通过执行感知方法映射句子到语义结构，并通过跨语言词对齐和子结构投影方法改进零样本跨语言依赖分析的性能。