Semantic and Syntactic Annotation of Child-directed Speech

Core Concepts

Proposing a methodology for consistent semantic and syntactic annotation of child-directed speech.

Abstract

The paper discusses the importance of semantic annotation in child language acquisition studies. It introduces a methodology for creating corpora of child-directed speech paired with logical forms. The approach enforces cross-linguistically consistent representation using Universal Dependencies (UD) scheme. The study focuses on syntactic and semantic phenomena in child-directed speech, comparing English and Hebrew corpora. The utility of the compiled corpora is demonstrated through longitudinal corpus studies and computational modeling of language acquisition.

Stats

"We annotate ≈80% of its child-directed utterances" "over 24K Hebrew utterances" "82% of the LFs in both languages are fully correct" "80.5% (English) and 72.7% (Hebrew)" "large contiguous portion of Brown’s Adam corpus from CHILDES"

Quotes

"We show that the UD scheme can be applied to CDS with some additional guidelines." "We compile two UD-annotated corpora of CDS, one in English and one in Hebrew." "We develop an automatic conversion method and codebase for converting UD-annotated CDS to logical forms."

Key Insights Distilled From

Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech

by Ida Szubert,... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2109.10952.pdf

Deeper Inquiries

How does the proposed methodology address the scarcity of semantic annotation in child language acquisition studies?

The proposed methodology addresses the scarcity of semantic annotation in child language acquisition studies by providing a systematic approach to annotating corpora of child-directed speech (CDS) with sentential logical forms. By pairing CDS with these logical forms, researchers can gain insights into the meaning representations conveyed in adult-child interactions. This is crucial for understanding how children acquire language from their input and developing computational models of language acquisition. Semantic annotation is essential for studying the nature of the input children receive and for modeling how they infer meaning representations from utterances. The methodology enforces a cross-linguistically consistent representation by using Universal Dependencies (UD) scheme for syntactic annotation and automatically transducing sentential logical forms from UD structures. This consistency across languages facilitates comparative studies and allows for more robust analyses of syntactic and semantic phenomena in CDS. By creating annotated corpora in multiple languages, such as English and Hebrew, with consistent semantic annotations, this methodology provides valuable resources for researchers working on child language acquisition. These annotated corpora enable detailed investigations into how children interpret linguistic input, paving the way for advancements in our understanding of early language development.

What are the implications of enforcing cross-linguistically consistent representation in child-directed speech annotation?

Enforcing cross-linguistically consistent representation in child-directed speech annotation has several important implications: Comparative Studies: Consistent annotations across different languages allow researchers to compare syntactic and semantic patterns in child-directed speech across diverse linguistic contexts. This comparative analysis can reveal universal aspects of language acquisition as well as language-specific variations. Model Development: Cross-linguistic consistency enables computational models of language acquisition to be applied more effectively across multiple languages. Models trained on data with consistent annotations can better capture general principles underlying early language learning processes. Generalizability: By ensuring that annotations are applicable to a wide range of languages, research findings based on these annotated corpora become more generalizable and applicable beyond specific linguistic contexts or populations. Methodological Rigor: Enforcing consistency helps maintain high standards in data collection and analysis within the field of child-language research, promoting methodological rigor and facilitating reproducibility across studies conducted in different languages. Overall, enforcing cross-linguistically consistent representation enhances the quality and reliability of research outcomes related to child-directed speech analysis while fostering collaboration among researchers working on multilingual datasets.

How might variations in syntactic and semantic phenomena across languages impact computational models...

...of Language Acquisition? Variations in syntactic and semantic phenomena across languages can significantly impact computational models' performance when applied to different linguistic contexts: Transfer Learning Challenges: Computational models trained on one language may struggle when applied directly to another due to differences in syntax or semantics between them. 2....