NLI (Natural Language Inferencing)

Sherlock Holmes | Baker Street Wiki | Fandom

"…when you have eliminated the impossible, whatever remains, however improbable, must be the truth" Sir Arthur Conan Doyle

Our brains process the meaning of a sentence like this rather quickly.

We're able to surmise:

Some things to be true: "You can find the right answer through the process of elimination.”
Others that may have the truth: "Ideas that are improbable are not impossible!"
And some claims are clearly contradictory: "Things that you have ruled out as impossible are where the truth lies."

Natural language processing (NLP) has grown increasingly elaborate over the past few years. Machine learning models tackle question answering, text extraction, sentence generation, and many other complex tasks. But, can machines determine the relationships between sentences, or is that still left to humans? If NLP can be applied between sentences, this could have profound implications for fact-checking, identifying fake news, analyzing text, and much more.

If you have two sentences, there are three ways they could be related: one could entail the other, one could contradict the other, or they could be unrelated. Natural Language Inferencing (NLI) is a popular NLP problem that involves determining how pairs of sentences (consisting of a premise and a hypothesis) are related.

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