Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention
TABLE IV: Cross-corpus results (F-score in %). Rows correspond to training corpora and columns to testing. Models marked with † represents tLSTM and ‡ represents tLSTM+tAttn
['[EMPTY]', '[ITALIC] AIMed', '[ITALIC] BioInfer', '[ITALIC] IEPA', '[ITALIC] HPRD50', '[ITALIC] LLL']
Rows correspond to the training corpora and columns correspond to the test corpora. It is clearly visible that the performance degrades on all of the corpora as the training and testing sets are not from the same distribution which goes against the fundamental machine learning theory about training and test sets being identically distributed. Being larger in size, the models that are trained on BioInfer perform better than the models trained on AIMed. One more interesting aspect of our evaluation is that the models without attention perform better than the models with attention. The main reason is that our structured attention captures the syntactic dependencies in the sentences and because of the two different distributions between the training and testing sets, the attention mechanism fails to capture these dependencies.