Generating Safe Diversity in nlg via Imitation Learning
Table 2: Automatic evaluation results for greedy and previous works on variety.
['[EMPTY]', 'Greedy', 'Beam', 'FWC', 'MMI', 'SF']
We can see from the low numbers in the diversity metrics (1 - Self-BLEU and distinct-n) that none of these methods produce much output diversity in the task of concept-to-text NLG. Below, we provide some brief analysis on the results. Beam Search: similarly to what has been reported in open-domain nlg research Li et al. For example, for the mr [request(train-leave)] the top 3 beams obtained are “What time would you like to leave?”, “What time would you like to leave by?” and “What time would you like to depart?”. our approaches shows a much higher level of diversity in their output. However, we observe a significant drop in the word overlap metrics (BLEU-4, METEOR and MoverScore). To accurately determine the quality of the output, we will be performing human evaluation.