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Never-Ending Learning
Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a singleExpand
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Learning a Neural Semantic Parser from User Feedback
We present an approach to rapidly and easily build natural language interfaces to databases for new domains, whose performance improves over time based on user feedback, and requires minimalExpand
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Neural Semantic Parsing with Type Constraints for Semi-Structured Tables
We present a new semantic parsing model for answering compositional questions on semi-structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key technical innovations:Expand
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Incorporating Vector Space Similarity in Random Walk Inference over Knowledge Bases
Much work in recent years has gone into the construction of large knowledge bases (KBs), such as Freebase, DBPedia, NELL, and YAGO. While these KBs are very large, they are still very incomplete,Expand
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Jointly Learning to Parse and Perceive: Connecting Natural Language to the Physical World
This paper introduces Logical Semantics with Perception (LSP), a model for grounded language acquisition that learns to map natural language statements to their referents in a physical environment.Expand
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Weakly Supervised Training of Semantic Parsers
We present a method for training a semantic parser using only a knowledge base and an unlabeled text corpus, without any individually annotated sentences. Our key observation is that multiple formsExpand
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Instructable Intelligent Personal Agent
Unlike traditional machine learning methods, humans often learn from natural language instruction. As users become increasingly accustomed to interacting with mobile devices using speech, theirExpand
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Semantic Parsing to Probabilistic Programs for Situated Question Answering
Situated question answering is the problem of answering questions about an environment such as an image or diagram. This problem requires jointly interpreting a question and an environment usingExpand
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Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar
We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distantExpand
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Toward Interactive Grounded Language Acqusition
This paper addresses the problem of enabling robots to interactively learn visual and spatial models from multi-modal interactions involving speech, gesture and images. Our approach, called LogicalExpand
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