We present a syntactic dependency treebank for naturalistic child and
child-directed speech in English (MacWhinney, 2000). Our annotations largely
followed the guidelines of the Universal Dependencies project (UD (Zeman et
al., 2022)), with detailed extensions to lexical/syntactic structures unique to
conversational speech (in opposition to written texts). Compared to existing
UD-style spoken treebanks as well as other dependency corpora of child-parent
interactions specifically, our dataset is of (much) larger size (N of
utterances = 44,744; N of words = 233, 907) and contains speech from a total of
10 children covering a wide age range (18-66 months). With this dataset, we
ask: (1) How well would state-of-the-art dependency parsers, tailored for the
written domain, perform for speech of different interlocutors in spontaneous
conversations? (2) What is the relationship between parser performance and the
developmental stage of the child? To address these questions, in ongoing work,
we are conducting thorough dependency parser evaluations using both graph-based
and transition-based parsers with different hyperparameterization, trained from
three different types of out-of-domain written texts: news, tweets, and learner
data.

本研究提出了一个语法依存树库，用于儿童自然语言的注释和研究，分析了写作和口语之间的语言特征并探讨了儿童语言发展与句法分析器性能的关系。

基于数据的儿童 - 家长互动解析评估

Data-driven Parsing Evaluation for Child-Parent Interactions

Creating a descriptive grammar of a language is an indispensable step for
language documentation and preservation. However, at the same time it is a
tedious, time-consuming task. In this paper, we take steps towards automating
this process by devising an automated framework for extracting a first-pass
grammatical specification from raw text in a concise, human- and
machine-readable format. We focus on extracting rules describing agreement, a
morphosyntactic phenomenon at the core of the grammars of many of the world's
languages. We apply our framework to all languages included in the Universal
Dependencies project, with promising results. Using cross-lingual transfer,
even with no expert annotations in the language of interest, our framework
extracts a grammatical specification which is nearly equivalent to those
created with large amounts of gold-standard annotated data. We confirm this
finding with human expert evaluations of the rules that our framework produces,
which have an average accuracy of 78%. We release an interface demonstrating
the extracted rules at this https URL

本文以 Universal Dependencies 项目为例，通过开发一种自动化框架实现从原始文本中提取语法规范，并聚焦于提取描述协议的规则。我们使用跨语言转移的方法，即使在语言专家的注释不可获得的情况下，也能提取出几乎等同于大量黄金标准注释数据所创建的语法规范。