Predicting the Argumenthood of English Prepositional Phrases
Table 1: Test set performance on the binary classification task (n=4064).
['Classification model', 'w2v Acc.', 'w2v  [ITALIC] F1', 'Glove Acc.', 'Glove  [ITALIC] F1', 'fastText Acc.', 'fastText  [ITALIC] F1', 'ELMo Acc.', 'ELMo  [ITALIC] F1']
Vvp is a linear projection (d=512) of the output of an encoder (encoder states followed by max-pooling or a concatenated word vector), and σ is the softmax activation function. The output of Eq. The most trivial baseline is chance-level classification, which would yield 50% accuracy and F1. Since the labels in the dataset are perfectly balanced by randomized subsampling, majority class classification is equivalent to chance-level classification. All nontrivial models outperform chance, and out of all models tested ELMo yielded the best performance. Using concatenated inputs that preserve the linear order of the inputs increases both accuracy and F1 by around 1.5%p over bag-of-words, and using a BiLSTM to encode the V+P representation adds 1.5%p improvement across the board.