An Empirical Study on Explainable Prediction of Text Complexity: Preliminaries for Text Simplification
Table 7. Complexity prediction (BERT) and explanation (LIME & LSTM) performance on 20 scientific domains (Biendata/test), with lowest classification F1 and higher TER scores (a lower TER is better). Biomedical domains are harder for complexity prediction and explanation.
['[BOLD] Domain Name', '[BOLD] Classification', '[BOLD] Explanation']
We observe that in general the healthcare domain with its associated sub-fields is more difficult to be classified or highlighted correctly. On the one hand, this reveals the necessity to incorporate more and better quality parallel-aligned data in the medical domain; nevertheless, such parallel medical datasets in the English language are few, noisy, of limited size and not publicly available . On the other hand, results reveal the inherent difficulty of medical texts with all the technical jargon and outline the need to develop systems which can reliably translate highly specialized medical language into simpler language readily understandable by laypersons.