One of the dominant trends of artificial intelligence in the past decade has been to solve problems by creating ever-larger deep learning models. And nowhere is this trend more evident than in natural language processing, one of the most challenging areas of AI.
In recent years, researchers have shown that adding parameters to neural networks improves their performance on language tasks. However, the fundamental problem of understanding language—the iceberg lying under words and sentences—remains unsolved.
Linguistics for the Age of AI, a book by two scientists at Rensselaer Polytechnic Institute, discusses the shortcomings of current approaches to natural language understanding (NLU) and explores future pathways for developing intelligent agents that can interact with humans without causing frustration or making dumb mistakes.
Marjorie McShane and Sergei Nirenburg, the authors of Linguistics for the Age of AI, argue that AI systems must go beyond manipulating words. In their book, they make the case for NLU systems can understand the world, explain their knowledge to humans, and learn as they explore the world.
Consider the sentence, “I made her duck.” Did the subject of the sentence throw a rock and cause the other person to bend down, or did he cook duck meat for her?
Now consider this one: “Elaine poked the kid with the stick.” Did Elaine use a stick to poke the kid, or did she use her finger to poke the kid, who happened to be holding a stick?
Language is filled with ambiguities. We humans resolve these ambiguities using the context of language. We establish context using cues from the tone of the speaker, previous words and sentences, the general setting of the conversation, and basic knowledge about the world. When our intuitions and knowledge fail, we ask questions. For us, the process of determining context comes easily. But defining the same process in a computable way is easier said than done.
There are generally two ways to address this problem.
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