| Challenge: | Until recently, transition-based dependency parsers were limited to approximate inference due to their incompatibility with rich feature models. |
| Approach: | They propose a transition-based parser with high coverage on non-projective treebanks to support non- projective parsing. |
| Outcome: | The proposed approach is more efficient than its projective counterpart in non-projective languages. |
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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. |
Exploiting Dynamic Oracles to Train Projective Dependency Parsers on Non-Projective Trees (N18-2)
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| Challenge: | Several strategies have been proposed to overcome the projectivity constraint by introducing transition-based dependency parsers that can build non-projective dependencies. |
| Approach: | They propose a modification of dynamic oracles to allow use of non-projective data . their method consistently outperforms traditional projectivization and pseudo-projectivisation approaches . |
| Outcome: | The proposed method outperforms projectivization and pseudo-projectivisation methods on 73 treebanks and achieves significant gains for non-projective languages. |
Improving Coverage and Runtime Complexity for Exact Inference in Non-Projective Transition-Based Dependency Parsers (N18-2)
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| Challenge: | Non-projective dependency trees account for 12.59% of all training sentences in the annotated Universal Dependencies (UD) 2.1 data. |
| Approach: | They generalize Cohen et al.'s (2011) parser to a family of non-projective transition-based dependency parsers allowing polynomial-time exact inference. |
| Outcome: | The proposed system can be extended to include a variant that reduces time complexity to O(n6), improving over the known bounds in exact inference for non-projective transition-based parsing. |
A Transition-Based Algorithm for Unrestricted AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation language to map the meaning of English sentences into directed, cycled, labeled graphs. |
| Approach: | They propose a left-to-right non-projective transition-based parser that handles cycles and reentrancy natively within the transition system itself. |
| Outcome: | The proposed algorithm handles reentrancy and arbitrary cycles natively, i.e. within the transition system itself. |
A Unifying Theory of Transition-based and Sequence Labeling Parsing (2020.coling-main)
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| Challenge: | Existing parsers that read sentences from left to right are not learning to parse them. |
| Approach: | They propose a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. |
| Outcome: | The proposed algorithms are learnable and comparable to existing encodings. |
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. |
Headed-Span-Based Projective Dependency Parsing (2022.acl-long)
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| Challenge: | Existing methods for dependency parsing based on headed spans are available. |
| Approach: | They propose a method for projective dependency parsing based on headed spans. |
| Outcome: | The proposed method achieves state-of-the-art or competitive results on PTB, CTB, and UD Dependency parsing is an important task in natural language processing. |
Transition-Based Chinese AMR Parsing (N18-2)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic representation where the meaning of a sentence is encoded as a rooted, directed and acyclic graph. |
| Approach: | They propose a transition-based AMR parsing framework for Chinese to be used in the next generation of AMR. |
| Outcome: | The proposed parser is based on the Chinese AMR bank. |
Multitask Easy-First Dependency Parsing: Exploiting Complementarities of Different Dependency Representations (2020.coling-main)
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| Challenge: | Existing dependency parsing models for Arabic use complementary annotations, CATiB and UD treebanks, and partially created trees for one annotation are also available to the other as features for the score function. |
| Approach: | They propose to use Arabic dependency annotations to parse projective dependency trees using CATiB and UD treebanks. |
| Outcome: | The proposed model gives 9.9% error reduction on CATiB and 6.1% on UD compared to a strong baseline and ablation tests show that the main contribution is given by sharing tree representation between tasks, and not simply sharing biLSTM layers as is often performed in NLP multitask systems. |
Transition-based Bubble Parsing: Improvements on Coordination Structure Prediction (2021.acl-long)
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| Challenge: | Existing bubble representations encoding coordination boundaries and internal relationships are difficult to detect and parse . |
| Approach: | They propose a bubble parser to perform coordination structure identification and dependency-based syntactic analysis simultaneously. |
| Outcome: | The proposed bubble parser beats state-of-the-art approaches on coordination structure prediction . the proposed system is based on a GENIA corpus and a Penn treebank . |