BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactions
Table 6: RDoC Tasks official results - performance on test set of different competing systems. Best score in each column is marked in bold. (Refer to Table 2 for header notations) (mAP: “Mean Average Precision”; MAA: Macro-Average Accuracy)
['[BOLD] Team', '[BOLD] RDoC Task-1 (Official Results) L1', '[BOLD] RDoC Task-1 (Official Results) L2', '[BOLD] RDoC Task-1 (Official Results) L3', '[BOLD] RDoC Task-1 (Official Results) L4', '[BOLD] RDoC Task-1 (Official Results) L5', '[BOLD] RDoC Task-1 (Official Results) L6', '[BOLD] RDoC Task-1 (Official Results) L7', '[BOLD] RDoC Task-1 (Official Results) L8', '[BOLD] RDoC Task-1 (Official Results) mAP', '[BOLD] RDoC Task-2 (Official Results) L1', '[BOLD] RDoC Task-2 (Official Results) L2', '[BOLD] RDoC Task-2 (Official Results) L3', '[BOLD] RDoC Task-2 (Official Results) L4', '[BOLD] RDoC Task-2 (Official Results) L5', '[BOLD] RDoC Task-2 (Official Results) L6', '[BOLD] RDoC Task-2 (Official Results) L7', '[BOLD] RDoC Task-2 (Official Results) L8', '[BOLD] RDoC Task-2 (Official Results) MAA']
Observe that our submission (MIC-CIS) scored a mAP score of 0.86 and MAA of 0.58 in Task-1 and Task-2, respectively. Notice that we outperform the second best system by 20.83% (0.58 vs 0.48) margin in Task2.