Improving Semantic Parsing for Task Oriented Dialog
Table 3: Gains from LM-based re-ranking with beam size 5.
['Method', 'Accuracy']
The experiment in Gupta et al. We train the base model as described in the previous section, but use beam search of size 5 instead of greedy decoding at inference. This suggests that re-ranking the top hypotheses can be an effective way of increasing the accuracy. At the end, the top decoded beams are scored and re-ranked based on one of the two strategies described above. but there is still a huge difference compared to the oracle top-5 beam decoding. This suggests that more effective re-ranking approaches, such as including more features in the ranker, could be worth trying.