text: 120,49,1205496598433689600,119837224,Jason Baldridge,"New paper on extending ML models toward human-level language understanding! It's a joint effort with Jay McClelland, @FelixHill84, Maja Rudolph, and Hinrich Schütze that integrates our diverse perspectives on cognition, grounding, modeling and language. <LINK> <LINK> Key takeaway: we have seen tremendous progress with estimation of deep, contextualized models, and we argue for a renewed focus on modeling situations and objects, inspired by cognitive models and driven by grounding in active environments. <LINK> Both Jay and I are speaking today at the ViGIL workshop and will both cover aspects of this and our own perspectives in these general topics! <LINK> @FelixHill84 Thanks! @volkancirik Will post later! Bug me if you don’t see them. :-) @texastacos @Lextremist @FelixHill84 Wombats are also so cute! And they have cube-shaped poop!",https://arxiv.org/abs/1912.05877,"Language is crucial for human intelligence, but what exactly is its role? We take language to be a part of a system for understanding and communicating about situations. The human ability to understand and communicate about situations emerges gradually from experience and depends on domain-general principles of biological neural networks: connection-based learning, distributed representation, and context-sensitive, mutual constraint satisfaction-based processing. Current artificial language processing systems rely on the same domain general principles, embodied in artificial neural networks. Indeed, recent progress in this field depends on \emph{query-based attention}, which extends the ability of these systems to exploit context and has contributed to remarkable breakthroughs. Nevertheless, most current models focus exclusively on language-internal tasks, limiting their ability to perform tasks that depend on understanding situations. These systems also lack memory for the contents of prior situations outside of a fixed contextual span. We describe the organization of the brain's distributed understanding system, which includes a fast learning system that addresses the memory problem. We sketch a framework for future models of understanding drawing equally on cognitive neuroscience and artificial intelligence and exploiting query-based attention. We highlight relevant current directions and consider further developments needed to fully capture human-level language understanding in a computational system. ","Extending Machine Language Models toward Human-Level Language