UD - E nglish- CHILDES : A Collected Resource of Gold and Silver U niversal D ependencies Trees for Child Language Interactions (2025)

Abstract: CHILDES is a widely used resource of transcribed child and child-directed speech. This paper introduces UD-English-CHILDES, the first officially released Universal Dependencies (UD) treebank. It is derived from previously dependency-annotated CHILDES data, which we harmonize to follow unified annotation principles. The gold-standard trees encompass utterances sampled from 11 children and their caregivers, totaling over 48K sentences (236K tokens). We validate these gold-standard annotations under the UD v2 framework and provide an additional 1M silver-standard sentences, offering a consistent resource for computational and linguistic research.

## introduction the child language data exchange system (childes) @xcite has long been a key resource for research in language acquisition, computational modeling of child language, and the evaluation of natural language processing (nlp) tools. however, many analyses rely on different grammatical assumptions (e.g., @xcite @xcite @xcite , and therefore adopt divergent annotation frameworks or standards. while most existing annotations use syntactic dependencies-in part due to the relative simplicity of annotation and parsing and the growing adoption of the universal dependencies (ud) framework @xcite )-annotation practices remain inconsistent across datasets. this is largely due to the lack of a unified guideline for annotating children's speech, which presents unique challenges not fully addressed by existing ud documentation. as ud treebanks have become valuable resources in both nlp (e.g., @xcite and language acquisition research (e.g., @xcite and a a green one . figure 1 : ud tree for a child utterance from lily (providence corpus, @xmath0, there have been increasing efforts to parse childes data using tools such as stanza @xcite . however, the resulting annotation quality is often inconsistent and cannot be guaranteed. in this paper, we compile, harmonize, and manually correct major ud-style annotations of childes data into a consistent, unified ud format, resulting in a gold-standard treebank of 48k sentences and 236k tokens (including, e.g., the tree in figure 1 ). in addition, we construct a larger silver-standard treebank of 1m sentences and 6m tokens produced by stanza 1 and report parser accuracy estimates. we publicly release both datasets. 2 2 related work childes has also emerged as a valuable resource for nlp tool benchmarking and language model pretraining. following the work of @xcite , studies such as @xcite have highlighted the challenges faced by ud parsers when applied to child-directed speech, showing substantial performance gaps compared to adult data. childes also supports recent research on pretraining dynamics @xcite and the development of efficient language models, including in initiatives like the babylm challenge @xcite . ## conclusion & future work in this paper, we present the first harmonized ud treebanks for childes, covering 11 corpora and over 48k sentences from both child-directed and child-produced speech. the three datasets we compiled do not preserve conversational structure, and as a result, the finalized gold-standard treebank lacks coherent dialogue sequencing. preserving such structure would require additional manual annotation to make sure all sentences are gold. however, since our annotations include the original_sent_id field, reconstructing the conversation structure is straightforward. furthermore, morphological features have not been annotated or independently verified. future work will focus on further corrections to the silver-standard data and the continued expansion of the treebanks. we welcome collaboration on this ongoing effort.