Improving Neural Parsing by Disentangling Model Combination and Reranking Effects

ACL 2017

Improving Neural Parsing by Disentangling Model Combination and Reranking Effects

Jan 27, 2021
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Abstract: Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data. Authors: Daniel Fried, Mitchell Stern, Dan Klein (University of California, Berkeley)

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