Tracking Discrete and Continuous Entity State forProcess Understanding
Table 2: Results on the sentence-level (Task-1) and document-level (Task-2) evaluation task of the ProPara dataset on the test set. Our proposed CRF-based model achieves state of the art results on both the tasks compared to the previous work in Das et al. (2019). Incorporating ELMo further improves the performance for the state tracking module, as we see from the gains in Cat-1 and Cat-2.
['[BOLD] Model', 'Task-1  [BOLD] Cat-1', 'Task-1  [BOLD] Cat-2', 'Task-1  [BOLD] Cat-3', 'Task-1  [BOLD] Macro-Avg', 'Task-1  [BOLD] Micro-Avg', 'Task-2  [BOLD] Precision', 'Task-2  [BOLD] Recall', 'Task-2  [ITALIC] F1']
The performance gain is attributed to the gains in Cat-1 and Cat-2 (7.69% and 4.57% absolute), owing to the structural constraints imposed by the CRF layer. The gain in Cat-3 is relatively lower as it is the only sub-task involving location tracking. Additionally, using the frozen ELMo embedding the performance further improves with major improvements in Cat-1 and Cat-2. What are the inputs to the process? (ii) What are the outputs of the process? (iii) What conversions occur, when and where? (iv) What movements occur, when and where? We achieve state of the art results with a F1 of 58.6.