Children's language acquisition from the visual world is a real-world example of continual learning from dynamic and evolving environments; yet we lack a realistic setup to study neural networks' capability in human-like language acquisition . In this paper, we propose a realistic setup by simulating children's language acquisition process. We formulate language acquisition as a masked language modeling task where the model visits a stream of data with continuously shifting distribution. Our training and evaluation encode two important challenges in human's language learning, namely the continual learning and the compositionality. We show the performance of existing continual learning algorithms is far from satisfactory. We also study the interactions between memory based continual learning algorithms and compositional generalization and conclude that overcoming overfitting and compositional overfitting may be crucial for a good performance in our problem setup.Our code and data can be found at URL
Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this human-like language acquisition ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes. In the task, models are trained on a paired image-caption stream which has shifting object distribution; while being constantly evaluated by a visually-grounded masked language prediction task on held-out test sets. VisCOLL compounds the challenges of continual learning (i.e., learning from continuously shifting data distribution) and compositional generalization (i.e., generalizing to novel compositions). To facilitate research on VisCOLL, we construct two datasets, COCO-shift and Flickr-shift, and benchmark them using different continual learning methods. Results reveal that SoTA continual learning approaches provide little to no improvements on VisCOLL, since storing examples of all possible compositions is infeasible. We conduct further ablations and analysis to guide future work.
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Children's language acquisition from the visual world is a real-world example of continual learning from dynamic and evolving environments; yet we lack a realistic setup to study neural networks' capability in
Humans acquire language continually with much more limited access to data samples at a time, as compared to contemporary NLP systems. To study this
R
. In this paper, we propose a realistic setup by simulating children's language acquisition process. We formulate language acquisition as a masked language modeling task where the model visits a stream of data with continuously shifting distribution. Our training and evaluation encode two important challenges in human's language learning, namely the continual learning and the compositionality. We show the performance of existing continual learning algorithms is far from satisfactory. We also study the interactions between memory based continual learning algorithms and compositional generalization and conclude that overcoming overfitting and compositional overfitting may be crucial for a good performance in our problem setup.Our code and data can be found at URL
ability, we present VisCOLL, a visually grounded language learning task, which simulates the continual acquisition of compositional phrases from streaming visual scenes. In the task, models are trained on a paired image-caption stream which has shifting object distribution; while being constantly evaluated by a visually-grounded masked language prediction task on held-out test sets. VisCOLL compounds the challenges of continual learning (i.e., learning from continuously shifting data distribution) and compositional generalization (i.e., generalizing to novel compositions). To facilitate research on VisCOLL, we construct two datasets, COCO-shift and Flickr-shift, and benchmark them using different continual learning methods. Results reveal that SoTA continual learning approaches provide little to no improvements on VisCOLL, since storing examples of all possible compositions is infeasible. We conduct further ablations and analysis to guide future work.