Papers by Daniel Fernández-González
Transition-based Semantic Dependency Parsing with Pointer Networks (2020.acl-main)
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| Challenge: | Existing dependency parsers cannot be directly applied, so they need to be adaptable to deal with the absence of singlehead and connectedness constraints. |
| Approach: | They propose a transition system that produces labelled directed acyclic graphs and performs semantic dependency parsing with Pointer Networks. |
| Outcome: | The proposed system outperforms graph-based models and outperformed existing models on a harder NLP problem. |
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. |
A Dynamic Oracle for Linear-Time 2-Planar Dependency Parsing (N18-2)
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| Challenge: | Existing dynamic oracles for greedy parsers can handle non-projective syntax, but none are available for these types of training. |
| Approach: | They propose an efficient dynamic oracle for training the 2-Planar transition-based parser with over 99% coverage on non-projective syntactic corpora. |
| Outcome: | The proposed model outperforms the static training strategy in the vast majority of languages tested and scored better on most datasets than the arc-hybrid parser enhanced with the Swap transition. |
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. |
Non-Projective Dependency Parsing with Non-Local Transitions (N18-2)
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| Challenge: | Existing greedy transition-based parsers are prone to error propagation when creating arcs involving multiple transitions. |
| Approach: | They propose a greedy transition-based parser that introduces non-local transitions that create arcs involving nodes to the left of the current focus positions. |
| Outcome: | The proposed system outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers. |
Dynamic Oracles for Top-Down and In-Order Shift-Reduce Constituent Parsing (D18-1)
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| Challenge: | Top-down and in-order shift-reduce constituent parsers are the most accurate known shift-reducing algorithms for constituent paring. |
| Approach: | They propose to use dynamic oracles to train two of the most accurate shift-reduce algorithms for constituent parsing. |
| Outcome: | The proposed top-down and in-order shift-reduce parsers improve on the WSJ benchmark. |
Reducing Discontinuous to Continuous Parsing with Pointer Network Reordering (2021.emnlp-main)
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| Challenge: | Existing discontinuous constituent parsers are slow and lack accuracy and speed . however, discontinuous parsing can be solved by any off-the-shelf continuous parser . |
| Approach: | They propose to reduce discontinuous constituent parsing to a continuous problem by reordering tokens. |
| Outcome: | The proposed method is on par with state-of-the-art methods but considerably faster. |