PoMo: Generating Entity-Specific Post-Modifiers in Context
Table 5: Baseline model performance on the claim selection task.
['[EMPTY]', '[BOLD] Prec.', '[BOLD] Recall', '[ITALIC] F1']
Both neural baselines perform considerably better than the most-common claims model. This indicates that the provided contexts and claim values contain useful information for claim selection that goes beyond the information captured by global statistics of the dataset alone. We additionally observe that the ranking-based approach outperforms the classification-based approach in terms of both precision and F1 score, while having only slightly worse recall.