| Challenge: | a new method for nonlocal dependency identification is proposed for the Penn Treebank (PTB) syntactic structures are represented as graphs due to nonlocal dependencies . |
| Approach: | They propose a method that approximates PTB graph-structured representations by trees . their method reduces nonlocal dependency identification and constituency parsing into single parser . |
| Outcome: | The proposed method outperforms the previous methods in nonlocal dependency identification. |
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Span-based discontinuous constituency parsing: a family of exact chart-based algorithms with time complexities from O(nˆ6) down to O(nˆ3) (2020.emnlp-main)
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| Challenge: | a novel chart-based parser for discontinuous constituency trees is proposed for span-based span parsing . it can process discontinuous constituent trees of block degree two, including ill-nested structures . |
| Approach: | They propose a chart-based algorithm for span-based parsing of discontinuous constituency trees . they build variants with smaller search spaces and time complexities ranging from O(n6) down to O(N3) . |
| Outcome: | The proposed algorithm can process 98% of constituents in linguistic treebanks while having the same complexity as continuous constituency parsers. |
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)
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| Challenge: | Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. |
| Approach: | They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances. |
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N-ary Constituent Tree Parsing with Recursive Semi-Markov Model (2021.acl-long)
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| Challenge: | Existing graph-based constituent parsing methods generate hidden nodes with the dummy label inside the n-ary nodes to transform the tree into a binary tree for prediction. |
| Approach: | They propose a graph-based constituent parsing framework that uses a 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate. |
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Constituent Parsing as Sequence Labeling (D18-1)
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| Challenge: | Constituent parsing is a core problem in NLP where the goal is to obtain the syntactic structure of sentences expressed as a phrase structure tree. |
| Approach: | They propose a method to reduce constituent parsing to sequence labeling by using a tree with unary branches. |
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Stack-Pointer Networks for Dependency Parsing (P18-1)
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| Challenge: | Existing approaches to dependency parsing are local and greedy transitionbased . StackPtr parsers use the information of whole sentences and previously derived subtree structures . |
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Efficient Constituency Parsing by Pointing (2020.acl-main)
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| Challenge: | Constituency parsing is a core task in natural language processing (NLP) Existing methods for constituency paring are greedy transition-based or globally optimized. |
| Approach: | They propose a constituency parsing model that casts the problem into a series of pointing tasks. |
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A Root of a Problem: Optimizing Single-Root Dependency Parsing (2021.emnlp-main)
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| Challenge: | Graph-based dependency parsers can be improved without compromising on accuracy or accuracy. |
| Approach: | They propose two approaches to single-root dependency parsing that yield speed ups . they show that one approach is fully correct and finds the optimal dependency tree . |
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Probing for Constituency Structure in Neural Language Models (2022.findings-emnlp)
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| Challenge: | Using standard probing techniques, we examine whether contextual neural language models implicitly learn syntactic structure. |
| Approach: | They investigate to which extent contextual neural language models implicitly learn syntactic structure. |
| Outcome: | The proposed model is able to represent constituents of different categories within the neuron activations of a LM such as RoBERTa with high performance even on manipulated data. |
Please Mind the Root: Decoding Arborescences for Dependency Parsing (2020.emnlp-main)
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| Challenge: | a dependency tree has a root constraint, but only one edge may emanate from the root node. |
| Approach: | They propose an algorithm which enforces a root constraint without compromising the original runtime. |
| Outcome: | The proposed algorithm satisfies the constraint without compromising the original runtime. |
Supertagging the Long Tail with Tree-Structured Decoding of Complex Categories (2021.tacl-1)
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| Challenge: | Combinatory Categorial Grammar (CCG) parsers operate as a pipeline with a large search space of complex 'supertags' . |
| Approach: | They propose to use CCG supertags to generate CCG categories that have never been seen in training and to use tree-structured prediction to account for their internal structure. |
| Outcome: | The proposed model recovers a fraction of the long-tail supertags while approximating the state of the art in overall tag accuracy with fewer parameters. |