Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks
Table 2: Accuracies and F-scores from 5-fold cross validation of SciDT in various settings and a CRF. Simple attention corresponds to the out of context variant, and recurrent to the clause context variant.
['Model', 'Attention', 'Accuracy', 'F-score']
In addition, we also test a variant of our model that does not use attention in the input layer and the clause vectors are obtained simply as an average of the vectors of words in them. The performance of the averaged input SciDT is comparable to the CRF model, whereas the two attention models perform better. The performance of the recurrent attention SciDT model shows the importance of modeling context in attention.