Researchers from MIT have developed a semantic parser that learns through observation to more closely mimic a child’s language-acquisition process. This has potential to greatly extend computing’s capabilities. Science Daily brought this interesting information to us in their article, “Machines that learn language more like kids do.”
In computing, learning language is the task of syntactic and semantic parsers. These systems are trained on sentences annotated by humans that describe the structure and meaning behind words. Parsers are becoming increasingly important for web searches, natural-language database querying, and voice-recognition systems such as Alexa and Siri.
Children, however, learn language by observing their environment, listening to the people around them, and connecting the dots between what they see and hear.
Gathering the annotation data for computing to learn language can be time-consuming and difficult for less common languages. The researchers propose a parser that learns through observation to more closely mimic a child’s language-acquisition process, which could greatly extend the parser’s capabilities.
Melody K. Smith
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