Segmenting Scientific Abstracts into Discourse Categories
Table 5. Experiment with noisy data.
['Details', 'Accuracy on  [ITALIC] cs.NI test subset']
To test this hypothesis, we performed an experiment. We took the model fine-tuned on cs.combined dataset and used it to predict the labels for 150 additional abstracts from arXiv. CS.NI. These abstracts had no corresponding golden labels but we expect the predictions to have accuracy comparable to those of the test subset of cs.combined. We augmented the cs. NI training subset, so that now it contains 110+150=260 examples (albeit of somewhat questionable integrity). Then, the model that was pre-trained on PubMed was fine-tuned with this augmented corpus. We observed a 2% increase in accuracy on the the original cs. This vindicates our hypothesis.