Core Semantic First: A Top-down Approach for AMR Parsing The work described in this paper is substantially supported by a grant from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Code: 14204418). The first author is grateful for the discussions with Zhisong Zhang and Zhijiang Guo.
Table 2: The effect of different sibling orders.
['Order', 'Smatch weighted', 'Smatch core', 'Smatch ordinary']
We experiment with the following traversal variants: (1) random, which sorts the sibling nodes in completely random order. (2) relation freq. , which sorts the sibling nodes according to their relations to the head node. We assign higher priorities to relations that occur more frequently, which drives our parser always to seek for the most common relation first. (3) combined, which combines the above two strategies by using random and relation freq. with equal chance. achieves better performance than random order. Interestingly, the combined strategy significantly boosts the performance. The reason is that the random order potentially produces a larger set of training pairs since each random order strategy can be considered as a different training pair. On the other hand, the deterministic order stabilizes the maximum likelihood estimate training. Therefore, the combined strategy benefits from both worlds.