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I had to look up textual entailment (on wikipedia) because I wasn't sure of its formal definition. It turns out, it doesn't have one: >> "t entails h" (t ⇒ h) if, typically, a human reading t would infer that h is most likely true" So in other words it's down to good old eyballing. I'm not impressed, but not surprised either, it's just one of the many poorly defined tasks in machine learning, particularly NLP which has turned into a quagmire of shoddy work ever since people started firing linguists to improve their systems' performance. Anyway, since logical entailment is central to my field of study I can tell that if textual entailment is less strictly defined than logical entailment (as per the wikipedia article), then it doesn't require anything that we could recognise as "understanding". Because logical entailment certainly doesn't require understanding and its definition is as strict, as a very strict thing [1]. I mean, I can see how loosening a requirement for precision of any justification of a decision that "A means B" can improve performance, but I can't see how it can improve understanding. Edit: I'm not sure we disagree, btw, sorry for the grumpy tone. I fully agree with your gist about explainability etc. ______________ [1] Roughly, "A |= B iff for each model M, of A, M is a model of B", where A and B are sets of first order logic formulae and a "model" in this context is a logical interpretation under which a set of formulae is true. A "logical interpretation" is a partition of a predicate's atoms to true and false. |