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Region-by-region surprisal
Sample item for Filler-Gap Dependencies (3 sentential embeddings)
Item
Condition
prefix comp embedding subj verb obj continuation
Item Condition prefix comp embedding subj verb obj continuation
1 what_gap I know what our mother said her friend remarked that the park attendant reported your friend threw into the trash can
1 that_gap I know that our mother said her friend remarked that the park attendant reported your friend threw into the trash can
1 what_no-gap I know what our mother said her friend remarked that the park attendant reported your friend threw the plastic into the trash can
1 that_no-gap I know that our mother said her friend remarked that the park attendant reported your friend threw the plastic into the trash can
Prediction performance for Vanilla LSTM on Filler-Gap Dependencies (3 sentential embeddings)
Accuracy
Formula
Description
AccuracyPredictionDescription
76.19% (673,what_no-gap/6,obj)>(671,that_no-gap/6,obj) We expect the object 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.
80.95% (674,what_gap/7,continuation)<(672,that_gap/7,continuation) We expect the continuation 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”).