Predicting the Argumenthood of English Prepositional Phrases
Table 3: 10-fold cross-validation results on the gradient argumenthood prediction task.
['[ITALIC] d≥1000 Model', 'w2v-wiki ( [ITALIC] d=1000) Pearson’s  [ITALIC] r', 'w2v-wiki ( [ITALIC] d=1000)  [ITALIC] R2', 'w2v-wiki ( [ITALIC] d=1000)  [ITALIC] R2 [ITALIC] adj', 'ELMo ( [ITALIC] d=1024) Pearson’s  [ITALIC] r', 'ELMo ( [ITALIC] d=1024)  [ITALIC] R2', 'ELMo ( [ITALIC] d=1024)  [ITALIC] R2 [ITALIC] adj']
Results from several off-the-shelf regressors are reported for comparison. ELMo embeddings again produced the best results among the embeddings used in Experiment 1 (although our ablation study reveals that using only word embeddings as predictors is not sufficient to obtain satisfactory results). We further speculated that the dimensionality of the embeddings may have impacted the results, and ran additional experiments using higher-dimensional embeddings that were publicly available. Higher-dimensional embeddings did indeed lead to performance improvements, even though the actual inputs given to the models were all PCA-reduced to d=5. From this observation, we could further improve upon the inital ELMo results. Results from the best model (w2v-wiki) are given in addition to the set of results using the same embeddings as the models in Experiment 1. This model uses 1000-d word2vec features with additional interaction features (multiplicative, subtractive) that improved dev set performance.