|TREND||DATASET||BEST METHOD||PAPER TITLE||PAPER||CODE||COMPARE|
Building intelligent agents that can communicate with and learn from humans in natural language is of great value.
We build a virtual agent for learning language in a 2D maze-like world.
This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain.
Previous work on grounded language learning did not fully capture the semantics underlying the correspondences between structured world state representations and texts, especially those between numerical values and lexical terms.
Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground.