Morphosyntactic Analysis for CHILDES
This work addresses the need for researchers in language development to compare language learning processes across different languages, though it is incremental as it builds on existing tools and frameworks.
The paper tackled the problem of lacking a consistent quantitative framework for comparing language learning across languages by applying the Universal Dependencies framework to provide morphosyntactic analysis for 27 languages in the CHILDES database, enabling deeper crosslinguistic studies.
Language development researchers are interested in comparing the process of language learning across languages. Unfortunately, it has been difficult to construct a consistent quantitative framework for such comparisons. However, recent advances in AI (Artificial Intelligence) and ML (Machine Learning) are providing new methods for ASR (automatic speech recognition) and NLP (natural language processing) that can be brought to bear on this problem. Using the Batchalign2 program (Liu et al., 2023), we have been transcribing and linking data for the CHILDES database and have applied the UD (Universal Dependencies) framework to provide a consistent and comparable morphosyntactic analysis for 27 languages. These new resources open possibilities for deeper crosslinguistic study of language learning.