A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
Table 4: Results on sentence-level task. Our approach is abbreviated as ProGraph. ProComp is the symbolic-based baseline and other systems are strong neural baselines.
['[EMPTY]', '[BOLD] Cat-1', '[BOLD] Cat-2', '[BOLD] Cat-3', '[BOLD] Macro-Avg', '[BOLD] Micro-Avg']
This observation indicates that ProGraph is better at both location tracking and state change prediction because it can better model the correlation between participants and other entities. ProGraph also significantly outperforms ProComp in all types of question, which proves that integrating symbolic with the neural models enhances the reasoning ability.