Entity-Enriched Neural Models for Clinical Question Answering
Table 4: Precision, Recall and F1-score for logical form prediction.
['[BOLD] Setting', '[BOLD] Dataset', '[BOLD] Precision', '[BOLD] Recall', '[BOLD] F1-score']
Does the model provide a supporting rationale via logical form (LF) prediction? We analyzed the performance of M-cERNIE on MADE-s and emrQA-s datasets for logical form prediction, as we saw most improvement in sentence setting (-s). We calculated macro-weighted precision, recall and F1-score for logical form classification. We analysed the confusion matrix of predicted LF and observed that the model mainly gets confused between the logical forms which convey similar semantic information as shown in Fig. This suggests that the model can efficiently identify important semantic information from the question, which is critical for efficient QA. During inference, the M-cERNIE models yield a rationale regarding a new test question (unseen paraphrase) by predicting the logical form of the question as an auxiliary task. For ex, the LF in Fig.