| 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. |
Similar Papers
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. |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
Copied to clipboard
| Challenge: | Sequence-to-sequence models have been used for natural language generation tasks such as machine translation and summarization. |
| Approach: | They propose to build a strong baseline based on general purpose sequence-to-sequence models for constituency parsing. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
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. |
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. |
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. |
Investigating Non-local Features for Neural Constituency Parsing (2022.acl-long)
Copied to clipboard
| Challenge: | Constituency parsers have been able to achieve competitive performance by using local features. |
| Approach: | They propose to inject non-local features into the training process of a local span-based parser by predicting constituent n-gram non-local patterns and ensuring consistency between constituents and local constituents. |
| Outcome: | The proposed method outperforms the self-attentive parser in multi-lingual and zero-shot cross-domain settings. |
LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to self-training rely on limited and potentially low-quality raw corpora. |
| Approach: | They propose to enhance self-training with the large language model to generate domain-specific raw corpora iteratively and introduce grammar rules that guide the LLM in generating raw corporeals and establish criteria for selecting pseudo instances. |
| Outcome: | The proposed method outperforms traditional methods regardless of the large language model's performance. |
BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing (2021.findings-acl)
Copied to clipboard
| Challenge: | Recent results show that pretrained language models can be used for many tasks with high accuracy and high performance. |
| Approach: | They propose two methods for automatically analysing discontinuous parsers' errors. |
| Outcome: | The proposed methods characterize errors of a state-of-the-art transition-based discontinuous parser and provide an overview of the contribution of BERT to this task. |
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin. |
Efficient Constituency Parsing by Pointing (2020.acl-main)
Copied to clipboard
| 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. |
| Outcome: | The proposed model achieves 92.78 F1 without pre-trained models, which is faster than existing models. |