Understanding how children process ambiguous words is a challenge because sense disambiguation depends on sentence context bottom-up and top-down aspects. Here, we seek insight into this phenomenon by investigating how such a competence might arise in large distributional learners (Transformers) that purport to acquire sense representations from language input in a largely unsupervised fashion. We investigated how sense disambiguation might be achieved using model representations derived from naturalistic child-directed speech. We tested a large pool of Transformer models, varying in their pretraining input size/nature as well as the size of their parameter space. Tested across three behavioral experiments from the developmental literature, we found that these models capture some essential properties of child sense disambiguation, although most still struggle in the more challenging tasks with contrastive cues. We discuss implications for both theories of word learning and for using Transformers to capture child language processing.

Children learning their mother tongue engage in interactive communication starting from the early stages of their development. In a large-scale study of transcribed child-caregiver conversations, we investigated the role of Communicative Feedback in response to children's grammatical errors. We found evidence for both positive and negative feedback signals that are useful for learning the grammar of one's native language: Caregivers are more likely to provide acknowledgments if an utterance is grammatical, and they are more likely to ask for clarification if an utterance is ungrammatical. Further, we investigate how children react in response to negative communicative feedback signals and find evidence that grammaticality is improved in direct follow-ups to negative feedback signals. This study provides the largest and most comprehensive evidence supporting the presence and effectiveness of communicative feedback signals in grammar learning, broadening the literature on communicative feedback in language acquisition more generally.

A crucial step in children's language development is the mastery of how to use language in context. This involves the ability to recognize and use major categories of speech acts (e.g., learning that a "question" is different from a "request"). The current work provides a quantitative account of speech acts' emergence in the wild. Using a longitudinal corpus of child-caregiver conversations annotated for speech acts (Snow et al.,1996), we introduced two complementary measures of learning based on both children's production and comprehension. We also tested two predictors of learning based on input frequency and the speech acts' quality of linguistic cues. We found that children's developmental trajectory differed largely between production and comprehension. In addition, development in both of these dimensions was not explained with the same predictors (e.g., frequency in child-directed speech was predictive of production, but not of comprehension). The broader impact of this work is to provide a computational framework for the study of communicative development where both measures and predictors of children's pragmatic development can be tested and compared.

Children start to communicate and use language in social interactions from the very early stages in development. This allows them to experiment with their current linguistic knowledge and receive valuable feedback from their interlocutors. We conducted a large-scale corpus study to examine the quality of positive and negative Communicative Feedback signals that caregivers provide in terms of time-contingent responses and clarification requests. We found evidence for the effect of such feedback in supporting children’s production of intelligible speech. The broad impact of this paper is to highlight how general social feedback mechanisms that govern human communication can also support child language acquisition.

An important step in children's socio-cognitive development is learning how to engage in coordinated conversations. This requires not only becoming competent speakers but also active listeners. This paper studies children's use of backchannel signaling (e.g., "yeah!" or a head nod) when in the listener's role during conversations with their caregivers via video call. While previous work had found backchannel to be still immature in middle childhood (i.e., 6 to 11 years of age), our use of both more natural/spontaneous conversational settings and more adequate controls allowed us to reveal that school-age children are strikingly close to adult-level mastery in many measures of backchanneling. The broader impact of this paper is to highlight the crucial role of social context in evaluating children's conversational abilities.

Studies of children's language use in the wild (e.g., in the context of child-caregiver social interaction) have been slowed by the time- and resource- consuming task of hand annotating utterances for communicative intents/speech acts. Existing studies have typically focused on investigating rather small samples of children, raising the question of how their findings generalize both to larger and more representative populations and to a richer set of interaction contexts. Here we propose a simple automatic model for speech act labeling in early childhood based on the INCA-A coding scheme (Ninio et al., 1994). After validating the model against ground truth labels, we automatically annotated the entire English-language data from the CHILDES corpus. The major theoretical result was that earlier findings generalize quite well at a large scale. Our model will be shared with the community so that researchers can use it with their data to investigate various question related to language use both in typical and atypical populations of children.