One of the key challenges in grounded language acquisition is resolving the intentions of the expressions. Typically the task involves identifying a subset of records from a list of candidates as the correct meaning of a sentence. While most current work assume complete or partial independence be-tween the records, we examine a scenario in which they are strongly related. By representing the set of potential meanings as a graph, we explicitly encode the relationships between the candidate meanings. We introduce a refinement algorithm that first learns a lexicon which is then used to remove parts of the graphs that are irrelevant. Experiments in a navigation domain shows that the algorithm successfully recovered over three quarters of the co...
This paper focuses on a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a se...
textBuilding a computer system that can understand human languages has been one of the long-standing...
Human communication is inevitably grounded in the real world. Existing work on natural language proc...
textCommunicating with natural language interfaces is a long-standing, ultimate goal for artificial ...
In the past, research on learning language models mainly used syntactic information during the learn...
We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a c...
Much is still unknown about how children learn language, but it is clear that they perform “grounded...
It is often assumed that ‘grounded’ learning tasks are beyond the scope of grammatical inference tec...
International audienceIn the past, research on learning language models mainly used syntactic inform...
Word learning models are typically evaluated as the problem of observing words together with sets of...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
We describe a model of grammar learning in which all linguistic units are grounded in rich conceptua...
This paper proposes solutions to two semantic learnability problems that have featured prominently i...
We describe a model of grammar learning in which all linguistic units are grounded in rich conceptua...
Understanding language in any form requires understanding connections among words, concepts, phrases...
This paper focuses on a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a se...
textBuilding a computer system that can understand human languages has been one of the long-standing...
Human communication is inevitably grounded in the real world. Existing work on natural language proc...
textCommunicating with natural language interfaces is a long-standing, ultimate goal for artificial ...
In the past, research on learning language models mainly used syntactic information during the learn...
We present Grammar-Based Grounded Lexicon Learning (G2L2), a lexicalist approach toward learning a c...
Much is still unknown about how children learn language, but it is clear that they perform “grounded...
It is often assumed that ‘grounded’ learning tasks are beyond the scope of grammatical inference tec...
International audienceIn the past, research on learning language models mainly used syntactic inform...
Word learning models are typically evaluated as the problem of observing words together with sets of...
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Comput...
We describe a model of grammar learning in which all linguistic units are grounded in rich conceptua...
This paper proposes solutions to two semantic learnability problems that have featured prominently i...
We describe a model of grammar learning in which all linguistic units are grounded in rich conceptua...
Understanding language in any form requires understanding connections among words, concepts, phrases...
This paper focuses on a system, Wolfie (WOrd Learning From Interpreted Examples), that acquires a se...
textBuilding a computer system that can understand human languages has been one of the long-standing...
Human communication is inevitably grounded in the real world. Existing work on natural language proc...