Child-directed speech (CDS) is a particular type of speech that adults use
when addressing young children. Its properties also change as a function of
extralinguistic factors, such as age of the child being addressed. Access to
large amounts of representative and varied CDS would be useful for child
language research, as this would enable controlled computational modeling
experiments of infant language acquisition with realistic input in terms of
quality and quantity. In this study, we describe an approach to model
age-dependent linguistic properties of CDS using a language model (LM) trained
on CDS transcripts and ages of the recipient children, as obtained from North
American English corpora of the CHILDES database. The created LM can then be
used to stochastically generate synthetic CDS transcripts in an age-appropriate
manner, thereby scaling beyond the original datasets in size. We compare
characteristics of the generated CDS against the real speech addressed at
children of different ages, showing that the LM manages to capture
age-dependent changes in CDS, except for a slight difference in the effective
vocabulary size. As a side product, we also provide a systematic
characterization of age-dependent linguistic properties of CDS in CHILDES,
illustrating how all measured aspects of the CDS change with children's age.

通过使用在 CHILDES 数据库的北美英语语料库中训练的语言模型（LM）对 CDS 转录和接收儿童年龄进行建模，本研究描述了一种建模 CDS 年龄依赖性语言特征的方法，从而可以按年龄生成适合的合成 CDS 转录，捕捉到与 CDS 相关的年龄依赖性变化。

幼儿指导性言语的年龄相关分析与随机生成

Age-Dependent Analysis and Stochastic Generation of Child-Directed  Speech

Language models (LMs) have demonstrated remarkable proficiency in generating
linguistically coherent text, sparking discussions about their relevance to
understanding human language learnability. However, a significant gap exists
between the training data for these models and the linguistic input a child
receives. LMs are typically trained on data that is orders of magnitude larger
and fundamentally different from child-directed speech (Warstadt and Bowman,
2022; Warstadt et al., 2023; Frank, 2023a). Addressing this discrepancy, our
research focuses on training LMs on subsets of a single child's linguistic
input. Previously, Wang, Vong, Kim, and Lake (2023) found that LMs trained in
this setting can form syntactic and semantic word clusters and develop
sensitivity to certain linguistic phenomena, but they only considered LSTMs and
simpler neural networks trained from just one single-child dataset. Here, to
examine the robustness of learnability from single-child input, we
systematically train six different model architectures on five datasets (3
single-child and 2 baselines). We find that the models trained on single-child
datasets showed consistent results that matched with previous work,
underscoring the robustness of forming meaningful syntactic and semantic
representations from a subset of a child's linguistic input.

该研究探讨了使用单个儿童语言输入子集训练语言模型，以形成有意义的句法和语义表征，发现这种方法的学习性强度和可靠性。

单个儿童语言输入对可学习性的系统调查

A systematic investigation of learnability from single child linguistic  input

Second language acquisition (SLA) research has extensively studied
cross-linguistic transfer, the influence of linguistic structure of a speaker's
native language [L1] on the successful acquisition of a foreign language [L2].
Effects of such transfer can be positive (facilitating acquisition) or negative
(impeding acquisition). We find that NLP literature has not given enough
attention to the phenomenon of negative transfer. To understand patterns of
both positive and negative transfer between L1 and L2, we model sequential
second language acquisition in LMs. Further, we build a Mutlilingual Age
Ordered CHILDES (MAO-CHILDES) -- a dataset consisting of 5 typologically
diverse languages, i.e., German, French, Polish, Indonesian, and Japanese -- to
understand the degree to which native Child-Directed Speech (CDS) [L1] can help
or conflict with English language acquisition [L2]. To examine the impact of
native CDS, we use the TILT-based cross lingual transfer learning approach
established by Papadimitriou and Jurafsky (2020) and find that, as in human
SLA, language family distance predicts more negative transfer. Additionally, we
find that conversational speech data shows greater facilitation for language
acquisition than scripted speech data. Our findings call for further research
using our novel Transformer-based SLA models and we would like to encourage it
by releasing our code, data, and models.

利用 Transformer-based SLA 模型研究母语语言 (L1) 对于英语作为第二语言 (L2) 学习的积极和消极转移，使用一个包括五种语言的多语言数据集，表明语言家族距离预测了更多的负面转移并且口头语言数据比脚本语言数据更有助于语言习得。

用 BERT 模型建模第二语言习得：SLABERT Talk Pretty One Day

SLABERT Talk Pretty One Day: Modeling Second Language Acquisition with  BERT

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。