Learning from Non-Binary Constituency Trees via Tensor Decomposition (2020.coling-main)
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| Challenge: | a binarisation procedure changes the structure of constituency trees, furthering constituents that are not binary. |
| Approach: | They propose a binarised approach to binarise constituency trees by tensor-based models . they propose 'trunk-LSTM' model which exploits such a rich structure . |
| Outcome: | The proposed model performs well on different NLP tasks. |
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Parsing Headed Constituencies (2024.lrec-main)
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| 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. |
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. |
| 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. |
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 . |
| Approach: | They propose a model that implicitly learns to encode much of the same information as grammars and lexicons in the past. |
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Neural Combinatory Constituency Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to constituency parsing are based on symbolic engineering, but they are simplified by their adaptive distributed representation. |
| Approach: | They propose two fast combinatory models for constituency parsing: binary and multibranching. |
| Outcome: | The proposed models achieve an F1 score of 92.54 on Penn Treebank, speeding at 1327.2 sents/sec. |
Unsupervised Parsing via Constituency Tests (2020.emnlp-main)
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| Challenge: | Existing methods for unsupervised parsing rely on constituency tests . linguists can judge a sentence's grammatical validity by modifying it via some transformation . |
| Approach: | They propose a method for unsupervised parsing based on a constituency test . they specify a set of transformations and use an unsupervised neural acceptability model to make grammaticality decisions. |
| Outcome: | The proposed method achieves 62.8 F1 on the Penn Treebank test set, an improvement of 7.6 points over the previous best results. |
High-order Joint Constituency and Dependency Parsing (2024.lrec-main)
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| 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)
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| 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. |
Challenges to Open-Domain Constituency Parsing (2022.findings-acl)
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| 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. |
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A Learning-Based Dependency to Constituency Conversion Algorithm for the Turkish Language (2022.lrec-1)
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Büşra Marşan, Oğuz K. Yıldız, Aslı Kuzgun, Neslihan Cesur, Arife B. Yenice, Ezgi Sanıyar, Oğuzhan Kuyrukçu, Bilge N. Arıcan, Olcay Taner Yıldız
| Challenge: | a team of linguists manually annotated a set of constituency trees. |
| Approach: | They propose to create the first Turkish-based dependency-to-constituency conversion algorithm using a morphologic analyser and feature-based machine learning model. |
| Outcome: | The proposed algorithm can be used to generate new constituency treebanks and training data for NLP resources like constituency parsers. |
Discontinuous Combinatory Constituency Parsing (2023.tacl-1)
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| Challenge: | Discontinuous parsing is more challenging than continuous parsers because children can group with syntactic cousins in the sentence rather than its two adjacent neighbors. |
| Approach: | They extend a pair of combinator-based constituency parsers into a discontinuous pair . they use a swap action and biaffine attention to iteratively compose constituent vectors from word embeddings without any grammar constraints. |
| Outcome: | The proposed parsers achieve state-of-the-art discontinuous accuracy with a significant speed advantage over continuous parsing. |