TRACKING OF ENRICHED DIALOG STATES FOR FLEXIBLE CONVERSATIONAL INFORMATION ACCESS
Table 2: Results for delexicalisation-based RNN, NBT and EDST on WOZ2.0. +dict means using the semantic dictionary, -spec. means we feed the original word embedding matrix X instead of value-specific embedding matrix f3(v,u) to CNNs.
['Model', 'Turn-level Goal', 'Joint Goal', 'Request']
In WOZ2.0, since turn-level labels are available, we remove last belief state b from input in EDST. Note that using turn-level based labels usually gives better results than using accumulated belief state labels, since the model do not have to learn the effect of last belief state. A rule-based tracking scheme is used to accumulate the turn-level prediction: substitute the old value label with new one if it is not labeled as NOT_MEN. Our model outperforms all other methods and achieve the best results when combining the advatages of both semantic dictionary and pre-trained word vectors.