| Challenge: | a string of recent work has attempted to delve into the formal properties of neural network topology choices. |
| Approach: | They propose to use recurrent models to perform projective maximum spanning tree decoding . they also prove the lower bounds of projective maximal spanning trees . |
| Outcome: | The proposed model can perform better than Eisner's model, proving it impossible to predict a projective MST. |
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| Challenge: | Recent studies of the representational capacity of neural LMs have focused on their ability to recognize formal languages. |
| Approach: | They propose to connect recurrent neural networks (RNNs) as classifiers to finite-state automatas (FSAs) and a probabilistic FSA to characterize their representational capacity. |
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On Finding the K-best Non-projective Dependency Trees (2021.acl-long)
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| Challenge: | Existing work on finding the one-best dependency tree has not extended this to finding the K-best tree. |
| Approach: | They propose to simplify the K-best spanning tree algorithm by decoding the K best dependency trees with a root constraint. |
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What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
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| Challenge: | a number of differences have emerged between classical and modern constituency parsing approaches . structural components like grammars and feature-rich lexicons are becoming less central . recurrent neural networks have gained traction as a powerful and general purpose tool for representation . |
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Fast and Accurate Non-Projective Dependency Tree Linearization (2020.acl-main)
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| Challenge: | Existing methods for decoding dependency trees are 10 times faster than current ones. |
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Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
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| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
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From Partial to Strictly Incremental Constituent Parsing (2024.eacl-short)
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| Challenge: | Incremental NLP aims to learn and adapt partial representations as information unfolds, but studies on incremental approaches have focused on non-incremental approaches. |
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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. |
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. |
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The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
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| Challenge: | a language model that is syntax-aware can produce better samples, authors say . a recent study shows that neural approaches to syntax can perform unsupervised syntactic parsing . |
| Approach: | They propose to incorporate syntax into neural approaches in NLP to produce better samples . they find that the first time neural approaches were able to perform unsupervised syntactic parsing . |
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Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing (D18-1)
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| Challenge: | Neural machine translation (NMT) models are based on sequential decoding or serialisation of structured data into sequence. |
| Approach: | They propose a model that combines sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. |
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