Children learn language by observing their environment, listening to the people around them and connecting the dots between what they see and hear. Among other things, this helps children establish their language's word order, such as where subjects and verbs fall in a sentence. 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. Soon, they may also be used for home robotics. In a new paper, National Science Foundation-funded researchers describe a parser that learns through observation to more closely mimic a child's language-acquisition process, which could greatly extend the parser's capabilities. To learn the structure of language, the parser observes captioned videos, with no other information, and associates the words with recorded objects and actions. Given a new sentence, the parser can then use what it's learned about the structure of the language to accurately predict a sentence's meaning, without the video.
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