ViGGO: A Video Game Corpus for Data-To-Text Generationin Open-Domain Conversation
Table 5: Naturalness and coherence scores of our model’s generated outputs compared to the reference utterances, as per the human evaluation. ViGGOinf corresponds to the subset of inform DAs only.
['[EMPTY]', '[BOLD] Naturalness  [BOLD] Ref.', '[BOLD] Naturalness  [BOLD] Gen. utt.', '[BOLD] Coherence  [BOLD] Ref.', '[BOLD] Coherence  [BOLD] Gen. utt.']
The scores for the ViGGO utterances were overall higher than those for the E2E ones, which we understand as an indication of the video game data being more fluent and conversational. At the same time, we observed that the utterances generated by our model tended to score higher than the reference utterances, though significantly more so for the E2E dataset. This is likely a consequence of the ViGGO dataset being cleaner and less noisy than the E2E dataset.