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Symposium on Machine Learning in Speech and Language Processing (MLSLP)Bellevue, WA, USA |
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Symposium on Machine Learning in Speech and Language Processing (MLSLP)Bellevue, WA, USA |
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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 between 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 correct semantic content.
Index Terms: ambiguously supervised learning, grounded language acquisition
Bibliographic reference. Chen, David / Mooney, Raymond (2011): "Panning for gold: finding relevant semantic content for grounded language learning", In MLSLP-2011, 26-30.