Improving Neural Parsing by Disentangling Model Combination and Reranking Effects
Table 1: F1 on the development set for word-synchronous beam search when searching in the RNNG generative (RG) and LSTM generative (LM) models. Ka is set to 10×Kw.
['model', 'Word-synchronous beam size,  [ITALIC] Kw 10', 'Word-synchronous beam size,  [ITALIC] Kw 20', 'Word-synchronous beam size,  [ITALIC] Kw 40', 'Word-synchronous beam size,  [ITALIC] Kw 60', 'Word-synchronous beam size,  [ITALIC] Kw 80', 'Word-synchronous beam size,  [ITALIC] Kw 100']
In practice, we found it to be most effective to use a value for Kw that is a fraction of the value for Ka. In all the experiments we present here, we fix Ka=10×Kw, with Kw ranging from 10 to 100. RG has comparatively larger gains in performance between the larger beam sizes, while still underperforming LM, suggesting that more search is necessary in this model.