| Challenge: | evalb is used for constituency parsing evaluation, but imposes constraints and requires consistent tokenization and sentence boundary outcomes. |
| Approach: | They propose an evaluation system designed to compute PARSEVAL measures, offering a viable alternative to evalb commonly used for constituency parsing evaluation. |
| Outcome: | The proposed evaluation system is based on an alignment method that aligns sentences and words when discrepancies arise. |
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| 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. |
An Empirical Study of Building a Strong Baseline for Constituency Parsing (P18-2)
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| 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. |
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Structured Tree Alignment for Evaluation of (Speech) Constituency Parsing (2024.acl-long)
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| Challenge: | Recent work has proposed a new task of textless speech constituency parsing that uses textless parsers to parse spoken word boundaries over automatically recognized spoken word borders. |
| Approach: | They propose a metric that compares a constituency parse tree over spoken word boundaries with a ground-truth parser tree over written words. |
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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 . |
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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. |
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BERT-Proof Syntactic Structures: Investigating Errors in Discontinuous Constituency Parsing (2021.findings-acl)
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| 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. |
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SemBleu: A Robust Metric for AMR Parsing Evaluation (P19-1)
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| Challenge: | Abstract Meaning Representation (AMR) is a semantic formalism where the meaning of a sentence is encoded as a rooted, directed graph. |
| Approach: | They propose a metric that extends SMATCH to parse AMRs and does not suffer from search errors. |
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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. |
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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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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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