Although his most famous robot, Bender isn’t the only one that Matt Groening invented. In the 266th episode of The Simpsons, Lisa Simpson invented a “robot” named Linguo that was effectively a grammar-correcting doll. Linguo’s reflexive need to correct people’s grammar by repeating their words back at them leads to plenty of antics to fill a thirty minute episode, ranging from getting into a fight with a pair of mobsters to convincing Homer to feed it beer to proclaiming its own death.
Homer: Linguo dead?
Linguo: Linguo is dead.
Aside from a couple human-like actions played for gags, such as becoming embarrassed when Lisa points out that “Sentence fragment” is itself a sentence fragment, Linguo’s behavior is simplistic. It responds the same way in every circumstance, so it does not require a reinforcement learning network. It could use a transformer model to convert speech it hears into text and then compare it to an internal model of linguistic acceptability. Speech that fails the acceptability test, such as “Me want beer” would then be flagged for translation to standard English: “I want beer.” Linguo would then use text to speech to convert the new sentence back to speech.
Transformer models use a technique called “self attention” to dynamically understand each word in a sentence in the context of all the other words in the sentence. This allows them to make connections between words even if they’re a long distance apart. The attention mechanism also allows transformers to flexibly connect input and output, so if need be, Linguo can rearrange an entire sentence to make it grammatically correct, although in practice he usually just changes one word at a time.
But the real marvel? That an elementary school child put all this together in 2001. Lisa Simpson might have been the next Tony Stark if she was ever allowed to grow up.
Origin: The Simpsons, Episode 18, Season 12
Likely Architecture: Transformers for Speech and Language processing
Possible Training Domains: The Corpus of Linguistic Acceptability, Large quantities of standard text
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