Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts. We study the problem of compositional generalization, which measures how well a model composes unseen combinations of concepts when describing images. Stateof-the-art image captioning models show poor generalization performance on this task. We propose a multi-task model to address the poor performance, that combines caption generation and image-sentence ranking, and uses a decoding mechanism that re-ranks the captions according their similarity to the image. This model is substantially better at generalizing to unseen combinations of concepts compared to state-of-the-art captioning models.

We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task. The models are architecturally identical at inference time, but differ in the way that they are trained: our baseline model is trained with a task-success signal only, while the other model receives additional supervision on its attention mechanism (Attentive Guidance), which has shown to be an effective method for encouraging more compositional solutions . We first confirm that the models with attentive guidance indeed infer more compositional solutions than the baseline, by training them on the lookup table task presented by Liška et al. (2019). We then do an in-depth analysis of the structural differences between the two model types, focusing in particular on the organisation of the parameter space and the hidden layer activations and find noticeable differences in both these aspects. Guided networks focus more on the components of the input rather than the sequence as a whole and develop small functional groups of neurons with specific purposes that use their gates more selectively. Results from parameter heat maps, component swapping and graph analysis also indicate that guided networks exhibit a more modular structure with a small number of specialized, strongly connected neurons.

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 Zoom. While previous work had found backchannel to be still immature in middle childhood, 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.

When learning their native language, children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision. One possible learning mechanism is cross-situational learning, which has been successfully tested in laboratory experiments with children. Here we use Artificial Neural Networks to test if this mechanism scales up to more natural language and visual scenes using a large dataset of crowd-sourced images with corresponding descriptions. We evaluate learning using a series of tasks inspired by methods commonly used in laboratory studies of language acquisition. We show that the model acquires rich semantic knowledge both at the word-and sentence-level, mirroring the patterns and trajectory of learning in early childhood. Our work highlights the usefulness of low-level cooccurrence statistics across modalities in facilitating the early acquisition of higher-level semantic knowledge.

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 questions related to language use development.

The study of how children develop their conversational skills is an important scientific frontier at the crossroad of social, cognitive, and linguistic development with important applications in health, education, and child-oriented AI. While recent advances in machine learning techniques allow us to develop formal theories of conversational development in real-life contexts, progress has been slowed down by the lack of corpora that both approximate naturalistic interaction and provide clear access to children’s non-verbal behavior in face-to-face conversations. This work is an effort to fill this gap. We introduce ChiCo (for Child Conversation), a corpus we built using an online video chat system. Using a weakly structured task (a word-guessing game), we recorded 20 conversations involving either children in middle childhood (i.e., 6 to 12 years old) interacting with their caregivers (condition of interest) or the same caregivers interacting with other adults (a control condition), resulting in 40 individual recordings. Our annotation of these videos has shown that the frequency of children’s use of gaze, gesture, and facial expressions mirrors that of adults. Future modeling research can capitalize on this rich behavioral data to study how both verbal and non-verbal cues contribute to the development of conversational coordination

Recent computational models of the acquisition of spoken language via grounding in perception exploit associations between spoken and visual modalities and learn to represent speech and visual data in a joint vector space. A major unresolved issue from the point of ecological validity is the training data, typically consisting of images or videos paired with spoken descriptions of what is depicted. Such a setup guarantees an unrealistically strong correlation between speech and the visual data. In the real world the coupling between the linguistic and the visual modality is loose, and often confounded by correlations with non-semantic aspects of the speech signal. Here we address this shortcoming by using a dataset based on the children’s cartoon Peppa Pig. We train a simple bi-modal architecture on the portion of the data consisting of dialog between characters, and evaluate on segments containing descriptive narrations. Despite the weak and confounded signal in this training data, our model succeeds at learning aspects of the visual semantics of spoken language.

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, Snow, Pan, & Rollins, 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. Further, we introduced two complementary measures for the age of acquisition of speech acts which allows us to rank different speech acts according to their order of emergence in production and comprehension.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.