TRACKING OF ENRICHED DIALOG STATES FOR FLEXIBLE CONVERSATIONAL INFORMATION ACCESS
Table 3: Results for NBT, MemN2N and EDST on DSTC2.
['Model', 'Joint Goal', 'Request']
In DSTC2, we need to handle ASR problem. We train EDST on the transcripts and evaluate on the testset’s ASR N-best lists. Let Pi be the posterior probability for the i-th hypothesis at current turn, hypi be the i-th hypothesis utterance, the belief state is represented as follows: p(ξ,η|ASR)=N∑i=1Pi p(ξ,η|hypi) (7) Because the language usage in the DSTC2 dataset is less rich, adding semantic dictionary is less useful than in WOZ2.0. Our EDST achieves superior results.