Dynamic Head Selection for Neural Lexicalized Constituency Parsing (2025.acl-long)
Copied to clipboard
| Challenge: | Lexicalized parsing has traditionally been neglected in favor of unlexicalized, span-based methods. |
| Approach: | They propose a latent lexicalization framework that dynamically infers lexicals from data without relying on predefined head-finding rules. |
| Outcome: | The proposed model learns lexical dependencies directly from data, offering greater adaptability across languages and datasets. |
Similar Papers
What’s Going On in Neural Constituency Parsers? An Analysis (N18-1)
Copied to clipboard
| 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 . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
| Outcome: | The proposed model outperforms state-of-the-art models under similar conditions. |
Cross-Domain Generalization of Neural Constituency Parsers (P19-1)
Copied to clipboard
| Challenge: | Neural parsers perform well on in-domain benchmarks, but their performance degrades in well-understood ways. |
| Approach: | They analyze generalization on English and Chinese corpora to see if they can generalize to other domains. |
| Outcome: | The proposed neural parsers perform better on in-domain benchmarks than on out-of-domain corpora. |
Parsing Headed Constituencies (2024.lrec-main)
Copied to clipboard
| Challenge: | Using constituency and dependency trees, syntactic representations are preferred for tasks such as nominal phrase extraction and identification of terminology. |
| Approach: | They propose a parsing technique that generates headed constituency trees which combine information typically contained in constituency and dependency trees. |
| Outcome: | The proposed method generates headed constituency trees with discontinuities and can generate constituency tree with discontinuity. |
The Return of Lexical Dependencies: Neural Lexicalized PCFGs (2020.tacl-1)
Copied to clipboard
| Challenge: | Existing approaches to grammar induction focus on discovering constituents or dependencies. |
| Approach: | They propose to model lexical dependencies using context free grammars instead of lexicals . they show that this unified framework induces both constituents and dependencies . |
| Outcome: | The proposed model overcomes sparsity problems and induces constituents and dependencies better than the current methods. |
An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics. |
| Approach: | They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models . |
| Outcome: | The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models. |
The Limitations of Limited Context for Constituency Parsing (2021.acl-long)
Copied to clipboard
| 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 . |
| Outcome: | The proposed models can perform unsupervised syntactic parsing, but they are lagging behind . the proposed models are based on a sandbox of probabilistic context-free-grammars . |
Modeling Input Uncertainty in Neural Network Dependency Parsing (D18-1)
Copied to clipboard
| Challenge: | Recent advances in neural network parsers address data sparsity issues by modeling character level information and exploiting raw data in semi-supervised settings. |
| Approach: | They investigate whether lexical normalization provides similar functionality to lexiconal normalization . they show that a separate normalization component improves performance of a neural network parser . |
| Outcome: | The proposed approaches improve performance even with access to character level information and word embeddings. |
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
Copied to clipboard
| Challenge: | Existing findings on cross-domain constituency parsing are only made on a limited number of domains. |
| Approach: | They manually annotate a high-quality constituency treebank containing five domains and analyze challenges to open-domain constituency parsing using a set of linguistic features. |
| Outcome: | The proposed model significantly improves the performance of the proposed model on the domain-variant features. |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
Copied to clipboard
| Challenge: | Syntactic parsing aims to reveal how sentences are syntactically structured. |
| Approach: | They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase . |
| Outcome: | The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm . |
Revisiting the Practical Effectiveness of Constituency Parse Extraction from Pre-trained Language Models (2022.coling-1)
Copied to clipboard
| Challenge: | Constituency Parse Extraction from Pre-trained Language Models (CPE-PLM) is a new paradigm that attempts to induce constituency parse trees based on the internal knowledge of pre-tried language models. |
| Approach: | They propose to use constituency parse trees from pre-trained language models to induce constituency trees by introducing a set of heterogeneous PLMs combined using two advanced ensemble methods. |
| Outcome: | The proposed approach is more effective than typical supervised parsers in few-shot settings. |