Language learning is a multi-threaded, multi-mechanism process. It is multi-threaded in that it emerges as a byproduct of addressing multiple goals while engaging in social interactions. It is multi-mechanism in that children integrate multiple information sources to infer what is meant and what to say next. These information sources include contextual and social cues, as well as cognitive mechanisms. Focusing on early word learning, this article reviews information sources, how children might sensitively adapt to them, and how we can model their integration using Bayesian inference over multiple probability distributions. We argue that, to advance our understanding of language learning, we must jointly study how children learn from multiple information sources across diverse developmental settings.