Learning End-to-End Goal-Oriented Dialog
Table 8: Hyperparameters of Supervised Embeddings. When Use History is True, the whole conversation history is concatenated with the latest user utterance to create the input. If False, only the latest utterance is used as input.
['Task', 'Learning Rate', 'Margin  [ITALIC] m', 'Embedding Dim  [ITALIC] d', 'Negative Cand.  [ITALIC] N', 'Use History']
These models were selected using the best validation validation sets. When no history is used, we slightly adapt match type features to only record type: a special word corresponding to type T (e.g., phone, address, etc) is appended to the representation of a candidate if the candidate contains a word that appears in the knowledge base as an entity of type T, regardless of whether the same word appeared earlier in the conversation. Bigrams slightly hurt rather than help performance, except in Task 5 in the standard in-vocabulary setup (performance is lower in the OOV setup).