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Region-by-region surprisal
Sample item for Filler-Gap Dependencies (subject extraction)
Item
Condition
prefix comp np1 verb np2 prep np3 end
Item Condition prefix comp np1 verb np2 prep np3 end
1 what_nogap I know who our uncle grabbed the food in front of the guests at the holiday party
1 that_nogap I know that our uncle grabbed the food in front of the guests at the holiday party
1 what_gap I know who grabbed the food in front of the guests at the holiday party
1 that_gap I know that grabbed the food in front of the guests at the holiday party
Prediction performance for TinyLSTM on Filler-Gap Dependencies (subject extraction)
Accuracy
Formula
Description
AccuracyPredictionDescription
95.83% (678,what_gap/4,verb) < (675,that_gap/4,verb) We expect the “verb” region to be lower in the what_gap condition than in the that_gap condition, because gaps must be licensed by upstream wh words (such as “what”).
100.00% (676,what_nogap/3,np1) > (677,that_nogap/3,np1) We expect the NP1 to be less surprising in the that_no-gap condition than in the what_no-gap condition, because an upstream wh-word should set up an expectation for a gap.