A retrieval-based dialogue system utilizing utterance and context embeddings
TABLE III: Overall comparison of model precisions (in %). Confidence intervals (±95%) are shown next to the average performance.
['Approach', 'Model', 'Predicting assistant responses R@1', 'Predicting assistant responses R@2', 'Predicting assistant responses R@5', 'Predicting customer responses R@1', 'Predicting customer responses R@2', 'Predicting customer responses R@5']
As with the Ubuntu corpus, the generative and retrieval based approaches (CR, AR, CAR) are compared and additionally in this setting, they are also tested upon different embedding initialization approaches (HREDL, HREDG and HREDLG) The performance of the generative approaches, HREDL, HREDG, and HREDLG, are relatively similar. However, HREDL slightly outperforms the others. A reason for this could be that utilizing global domain knowledge to generate an answer is more difficult than using specific domain knowledge. Especially for a very homogeneous (and domain specific) corpus, giving standard answers can work better. Nonetheless, similarity comparisons, used by the NNS approaches, could still benefit from richer embeddings.