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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models
- russellbeattie 7y agoSlightly off topic, but the analogy of this to Deep Thought having to design a system to explain what the actual Question was is pretty amazing. Douglas Adams was incredibly prescient.
- ivan_ah 7y agoWow this is amazing. Check out their interactive demo that lets you see visualizations for various tasks: https://demo.allennlp.org/coreference-resolution/MTA3MDQ4Nw== https://demo.allennlp.org/coreference-resolution/MTA3MDQ4Nw=... Interpretability is super important... first for technical debugging, and even more important for giving domain experts a view of the model inner working (otherwise they have to just trust the ML model blindly).
- carljoseph 7y agoVery interesting demo. Thanks for sharing. Unfortunately it doesn't seem to parse Winograd schemas[0] correctly [0] https://en.wikipedia.org/wiki/Winograd_Schema_Challenge https://en.wikipedia.org/wiki/Winograd_Schema_Challenge
- lifeisstillgood 7y ago>>> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence. I fear that quite a lot of humans won't get that either - am i being overly pessimistic?
- londogard 7y agoAwesome, I love to see how there's coming more and more frameworks for interpretability. It's incredibly important, especially when selling your solution to higher-ups. There's another solution named LIME which seems to take a similar but more general approach, I like this more tailored idea as it'll probably give a better interpretation of the NLP questions.
- polm23 7y agoLIME's authors came out with a new method called Anchors last year: https://homes.cs.washington.edu/~marcotcr/aaai18.pdf https://homes.cs.washington.edu/~marcotcr/aaai18.pdf https://github.com/marcotcr/anchor https://github.com/marcotcr/anchor