The acquisition of grammar has been a central question to adjudicate between
theories of language acquisition. In order to conduct faster, more
reproducible, and larger-scale corpus studies on grammaticality in
child-caregiver conversations, tools for automatic annotation can offer an
effective alternative to tedious manual annotation. We propose a coding scheme
for context-dependent grammaticality in child-caregiver conversations and
annotate more than 4,000 utterances from a large corpus of transcribed
conversations. Based on these annotations, we train and evaluate a range of NLP
models. Our results show that fine-tuned Transformer-based models perform best,
achieving human inter-annotation agreement levels.As a first application and
sanity check of this tool, we use the trained models to annotate a corpus
almost two orders of magnitude larger than the manually annotated data and
verify that children's grammaticality shows a steady increase with age.This
work contributes to the growing literature on applying state-of-the-art NLP
methods to help study child language acquisition at scale.

通过自动标注工具对上千个关于儿童和照顾者对话的语料进行了上下文相关语法的编码和标注，并通过训练和评估一系列基于 Transformer 的 NLP 模型表明，在儿童语法中，细调的 Transformer 模型表现最佳，达到了人类间标注一致性水平，从而在研究儿童语言习得方面应用了现有最先进的 NLP 方法。

儿童保育者对话中的语法准确性自动标注

Automatic Annotation of Grammaticality in Child-Caregiver Conversations

This work explores the degree to which grammar acquisition is driven by
language `simplicity' and the source modality (speech vs. text) of data. Using
BabyBERTa as a probe, we find that grammar acquisition is largely driven by
exposure to speech data, and in particular through exposure to two of the
BabyLM training corpora: AO-Childes and Open Subtitles. We arrive at this
finding by examining various ways of presenting input data to our model. First,
we assess the impact of various sequence-level complexity based curricula. We
then examine the impact of learning over `blocks' -- covering spans of text
that are balanced for the number of tokens in each of the source corpora
(rather than number of lines). Finally, we explore curricula that vary the
degree to which the model is exposed to different corpora. In all cases, we
find that over-exposure to AO-Childes and Open Subtitles significantly drives
performance. We verify these findings through a comparable control dataset in
which exposure to these corpora, and speech more generally, is limited by
design. Our findings indicate that it is not the proportion of tokens occupied
by high-utility data that aids acquisition, but rather the proportion of
training steps assigned to such data. We hope this encourages future research
into the use of more developmentally plausible linguistic data (which tends to
be more scarce) to augment general purpose pre-training regimes.

本研究探讨了语法习得受语言 ' 简易性 ' 和数据来源模态（语音与文本）的影响程度。通过使用 BabyBERTa 作为探针，发现语法习得主要受到语音数据的影响，尤其是通过接触 AO-Childes 和 Open Subtitles 两个训练语料库。研究结果表明，过度接触 AO-Childes 和 Open Subtitles 显著提高了语法习得效果，并通过一项可比较的对照数据集得到验证。这些发现表明，对于习得，关键不在于高效数据所占的比例，而在于将训练步骤分配给此类数据的比例。希望这将促进将更具发展可行性的语言数据（通常更加稀缺）用于增强通用预训练制度的未来研究。

发展性数据课程学习对语法习得的影响

On the effect of curriculum learning with developmental data for grammar  acquisition

There has been recent interest in applying cognitively or empirically
motivated bounds on recursion depth to limit the search space of grammar
induction models (Ponvert et al., 2011; Noji and Johnson, 2016; Shain et al.,
2016). This work extends this depth-bounding approach to probabilistic
context-free grammar induction (DB-PCFG), which has a smaller parameter space
than hierarchical sequence models, and therefore more fully exploits the space
reductions of depth-bounding. Results for this model on grammar acquisition
from transcribed child-directed speech and newswire text exceed or are
competitive with those of other models when evaluated on parse accuracy.
Moreover, gram- mars acquired from this model demonstrate a consistent use of
category labels, something which has not been demonstrated by other acquisition
models.

本篇研究将深度限制的方法应用于概率无上下文语法归纳，并在语法获取方面取得了出色的结果，并且从该模型中获得的语法证明了对类别标签的一致使用。