Papers with English PTB
Left-to-Right Dependency Parsing with Pointer Networks (N19-1)
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| Challenge: | a new algorithm that parses sentences from left to right is simpler than the top-down stack-pointer parser . a graph-based dependency parsing model has been ahead of the curve in terms of accuracy in the past two years . |
| Approach: | They propose a transition-based algorithm that parses sentences from left to right by building n attachments, with n being the length of the input sentence. |
| Outcome: | The proposed algorithm outperforms the top-down stack-pointer parser and is twice as fast as the original top-up stack-pointing parsers. |
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data (D18-1)
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| Challenge: | Pre-trained word embeddings and language models cannot capture word connections in a sentence. |
| Approach: | They propose to implicitly capture word connections from unlabeled data by word ordering model with self-attention mechanism. |
| Outcome: | The proposed model achieves 96.35% UAS and 95.25% LAS on the English PTB dataset. |
Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing (2020.acl-main)
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| Challenge: | Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations have achieved the best accuracy to date. |
| Approach: | They propose to use an in-order shift-reduce linearization instead of a top-down tree linearization to represent trees as sequences. |
| Outcome: | The proposed approach achieves the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. |