Learning Semantics Workshop
TechTalks from event: Learning Semantics Workshop
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Learning Natural Language from its Perceptual ContextMachine learning has become the best approach to building systems that comprehend human language. However, current systems require a great deal of laboriously constructed human-annotated training data. Ideally, a computer would be able to acquire language like a child by being exposed to linguistic input in the context of a relevant but ambiguous perceptual environment. As a step in this direction, we have developed systems that learn to sportscast simulated robot soccer games and to follow navigation instructions in virtual environments by simply observing sample human linguistic behavior. This work builds on our earlier work on supervised learning of semantic parsers that map natural language into a formal meaning representation. In order to apply such methods to learning from observation, we have developed methods that estimate the meaning of sentences from just their ambiguous perceptual context.
Learning Dependency-Based Compositional SemanticsThe semantics of natural language has a highly-structured logical aspect. For example, the meaning of the question â€œWhat is the third tallest mountain in a state not bordering California?â€ involves superlatives, quantification, and negation. In this talk, we develop a new representation of semantics called Dependency-Based Compositional Semantics (DCS) which can represent these complex phenomena in natural language. At the same time, we show that we can treat the DCS structure as a latent variable and learn it automatically from question/answer pairs. This allows us to build a compositional question-answering system that obtains state-of-the-art accuracies despite using less supervision than previous methods. I will conclude the talk with extensions to handle contextual effects in language.
How to Recognize EverythingOur survival depends on recognizing everything around us: how we can act on objects, and how they can act on us. Likewise, intelligent machines must interpret each object within a task context. For example, an automated vehicle needs to correctly respond if suddenly faced with a large boulder, a wandering moose, or a child on a tricycle. Such robust ability requires a broad view of recognition, with many new challenges. Computer vision researchers are accustomed to building algorithms that search through image collections for a target object or category. But how do we make computers that can deal with the world as it comes? How can we build systems that can recognize any animal or vehicle, rather than just a few select basic categories? What can be said about novel objects? How do we approach the problem of learning about many related categories? We have recently begun grappling with these questions, exploring shared representations that facilitate visual learning and prediction for new object categories. In this talk, I will discuss our recent efforts and future challenges to enable broader and more flexible recognition systems.