ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents
Table 8. Ablation study for different entity representations.
['[BOLD] Dataset  [BOLD] Method', '[BOLD] Dataset Alexa', '[BOLD] Dataset Alexa', '[BOLD] Dataset Self-Dialogue', '[BOLD] Dataset Self-Dialogue']
Our model utilizes two variants of entity-type representations, namely entity-type distribution (TypeDist) and entity-type sequence modeling (Ent2Vec). We evaluate both entity representation vectors separately on both Alexa and Self-Dialogue datasets. Moreover, we report the result when different combinations of the entity representations are joined with the Utt2Vec network. While both representations contribute greatly to the classifier performance, the effects are greater in the Alexa dataset, due to the strong correlation between the entity-types and the user topics of interest.