Getting To Know You: User Attribute Extraction from Dialogues
Table 3: Results on user attribute extraction. Our model achieves the highest human evaluation score (statistically significant), outperforming other generation and retrieval models. * Note that the Gold row is the distant supervision data.
['[EMPTY]', '[BOLD] ACC', '[BOLD] F1', '[BOLD] BLEU-1', '[BOLD] Human']
Moreover, our model achieves the highest BLEU-1 score, 51.87, where all the generation models work better than KVMN. This is because KVMN has the limitation that it can only retrieve triplets that are pre-defined in the dataset, and cannot generate new triplets. We also conduct human evaluation over 100 randomly selected test samples. The results show that 67.11% of our generated user attributes can be inferred from the user utterances, which is significantly better than KVMN by 15.1%.