Can Neural Generators for Dialogue Learn Sentence Planning and Discourse Structuring?
Table 5: Sentence Scoping Results
['[BOLD] Model', '[BOLD] Slot  [BOLD] Error', '[BOLD] Period  [BOLD] Accuracy', '[BOLD] Period  [BOLD] Correlation']
Sentence Scoping Results. In the case of NoSup, we compare the number of sentences in the generated output to those in the corresponding test reference, and for PeriodCount, we compare the number of sentences in the generated output to the number of sentences we explicitly encode in the MR. The table shows that the NoSup setting fails to output the correct number of sentences in most cases (only a 22% accuracy), but the PeriodCount setting makes only 2 mistakes (almost perfect accuracy), demonstrating almost perfect control of the number of output sentences with the single-token supervision. We also show correlation levels with the gold-standard references (all correlations significant at p≤0.01).