# Data-driven Parsing Evaluation for Child-Parent Interactions

arXiv: https://arxiv.org/abs/2209.13778 · HTML: https://mlsanity.com/paper/2209.13778/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 76 references that are in the map.
- First posted on arXiv 28 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification](https://mlsanity.com/paper/2010.12421/) (2020)
- [Deep Biaffine Attention for Neural Dependency Parsing](https://mlsanity.com/paper/1611.01734/) (2016)
- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://mlsanity.com/paper/1603.04351/) (2016)
- [Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLP](https://mlsanity.com/paper/2005.14672/) (2020)
- [Parsing Tweets into Universal Dependencies](https://mlsanity.com/paper/1804.08228/) (2018)
- [Universal Dependencies for Learner English](https://mlsanity.com/paper/1605.04278/) (2016)
- [82 Treebanks, 34 Models: Universal Dependency Parsing with Multi-Treebank Models](https://mlsanity.com/paper/1809.02237/) (2018)
- [Mischievous Nominal Constructions in Universal Dependencies](https://mlsanity.com/paper/2108.12928/) (2021)
- [Dependency Parsing for Spoken Dialog Systems](https://mlsanity.com/paper/1909.03317/) (2019)
- [Replicating and Extending "Because Their Treebanks Leak": Graph Isomorphism, Covariants, and Parser Performance](https://mlsanity.com/paper/2106.00352/) (2021)
- [Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech](https://mlsanity.com/paper/2109.10952/) (2021)
- [UniParse: A universal graph-based parsing toolkit](https://mlsanity.com/paper/1807.04053/) (2018)

## Built on by (most cited first, 3 of 3)

- [UD-English-CHILDES: A Collected Resource of Gold and Silver Universal Dependencies Trees for Child Language Interactions](https://mlsanity.com/paper/2504.20304/) (2025)
- [CAIT: A Syntactic Parsing Toolkit for Child-Adult InTeractions](https://mlsanity.com/paper/2605.19718/) (2026)
- [Do Syntactic Categories Help in Developmentally Motivated Curriculum Learning for Language Models?](https://mlsanity.com/paper/2511.08199/) (2025)

## Abstract (arXiv)

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.