| Challenge: | Existing studies on unsupervised discourse parsing have shown that it is expensive, time-consuming, and sometimes highly ambiguous. |
| Approach: | They propose an unsupervised parsing algorithm using Viterbi EM with a margin-based criterion and initialization methods for Viterbia training of discourse constituents based on prior knowledge of text structures. |
| Outcome: | The proposed method outperforms fully supervised parsers in terms of performance and learning of discourse constituents. |
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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 . |
| 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. |
Co-training an Unsupervised Constituency Parser with Weak Supervision (2022.findings-acl)
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| Challenge: | Existing methods for unsupervised parsing that use bootstrapping classifiers to identify if a node dominates a span are lacking. |
| Approach: | They propose a method for unsupervised parsing that relies on bootstrapping classifiers to identify if a node dominates a specific span. |
| Outcome: | The proposed method achieves 63.1 F1 on the English test set and new state-of-the-art on treebanks for Chinese and Japanese. |
Adapting Unsupervised Syntactic Parsing Methodology for Discourse Dependency Parsing (2021.acl-long)
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| Challenge: | Discourse dependency parsing is a task that requires a large amount of training data, but there is little research on it. |
| Approach: | They propose to adapt unsupervised syntactic dependency parsing methods for unsupervised discourse dependency parses using unlabeled training data. |
| Outcome: | The proposed methods outperform existing methods in semi-supervised and supervised settings and outperformed existing methods. |
An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)
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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. |
On the Role of Supervision in Unsupervised Constituency Parsing (2020.emnlp-main)
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| Challenge: | Recent work on unsupervised constituency parsing uses labeled examples for tuning . a few-shot parser with labeles can outperform other approaches by a significant margin . |
| Approach: | They propose to use as few labeled examples as possible for model development . they propose to train existing models on the same labeles they access . |
| Outcome: | The proposed model outperforms other models on the WSJ development set by a significant margin . the proposed model can be further improved by augmentation and self-training . |
Rule Augmented Unsupervised Constituency Parsing (2021.findings-acl)
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| Challenge: | Recent studies have shown that unsupervised parsing methods do not learn meaningful semantics (not even simple grammar) |
| Approach: | They propose an approach that utilizes very generic linguistic knowledge of the language present in the form of syntactic grammar rules and is independent of the base system. |
| Outcome: | The proposed model is independent of the base system and takes advantage of syntactic grammar rules. |
A Unified Linear-Time Framework for Sentence-Level Discourse Parsing (P19-1)
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| Challenge: | a new neural framework for sentence-level discourse analysis is proposed . a discourse segmenter and a parser are based on pointer networks and operate in linear time . |
| Approach: | They propose a neural framework for sentence-level discourse analysis in accordance with Rhetorical Structure Theory . they use a discourse segmenter and a parser to construct a discursive tree in a top-down fashion . |
| Outcome: | The proposed framework surpasses previous approaches on both tasks and human agreement on both. |
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. |
| Outcome: | The proposed model outperforms existing models in natural language generation tasks without any explicit task-specific knowledge or architecture of constituent parsing. |
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining (2020.coling-main)
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| Challenge: | Discourse parsing is an important upstream task within the area of Natural Language Processing (NLP) . |
| Approach: | They propose a discourse parser that incorporates recent contextual language models to improve the performance of RST-based discourse parses. |
| Outcome: | The proposed parser outperforms existing models on two key RST datasets and on large-scale "silver-standard" discourse treebank MEGA-DT. |
Heads-up! Unsupervised Constituency Parsing via Self-Attention Heads (2020.aacl-main)
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| Challenge: | Existing approaches to analyze syntactic knowledge of pre-trained language models have been limited. |
| Approach: | They propose an unsupervised method that extracts constituency trees from PLM attention heads. |
| Outcome: | The proposed method outperforms existing approaches if no development set is present. |