| Challenge: | Rhetorical Structure Theory (RST) parsers have been based on supervised learning approaches that require an annotated corpus of sufficient size and quality. |
| Approach: | They propose two unsupervised methods that build an optimal RST tree based on a dissimilarity score function for splitting a text span into smaller ones and a similarity score for merging two adjacent spans into a large one. |
| Outcome: | The proposed method achieves the best score on English and German RST treebanks, around 0.8 F1 score, close to the previous supervised parsers. |
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RST Parsing from Scratch (2021.naacl-main)
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| Challenge: | Fig. 1 shows a document level discourse parser that performs top-down end-to-end parsing without requiring segmentation . |
| Approach: | They propose a top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory framework. |
| Outcome: | The proposed model outperforms existing methods in end-to-end parsing and parse with gold segmentation without handcrafted features. |
Split and Rephrase: Better Evaluation and Stronger Baselines (P18-2)
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| Challenge: | a dataset mapping a complex sentence to a sequence of sentences conveying the same meaning is challenging in NLP. |
| Approach: | They propose a neural split and a copy-mechanism to break a complex sentence into several shorter sentences that convey the same meaning. |
| Outcome: | The proposed model outperforms the baseline model by 8.68 BLEU and further improves on the task. |
Using and comparing Rhetorical Structure Theory parsers with rst-workbench (2021.eacl-demos)
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| Challenge: | Rhetorical Structure Theory (RST) parsers are usually only trained on English data . |
| Approach: | rst-workbench is a web-based tool that lets users install and use RST parsers. |
| Outcome: | rst-workbench is a web-based tool that lets users run multiple RST parsers simultaneously. |
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. |
Improving Neural RST Parsing Model with Silver Agreement Subtrees (2021.naacl-main)
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| Challenge: | Existing methods for Rhetorical Structure Theory (RST) parsing use supervised learning, but the RST-DT is small due to the costly annotation of RST trees. |
| Approach: | They propose to use silver data to improve RST parsing models by using annotated silver data. |
| Outcome: | The proposed method achieves the best micro-F1 scores for Nuclearity and Relation at 75.0 and 63.2 . it also achieves a remarkable gain in relation score against the previous state-of-the-art parser. |
Bilingual Rhetorical Structure Parsing with Large Parallel Annotations (2024.findings-acl)
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| Challenge: | Existing large RST corpora are inconsistent in annotation guidelines, genre representation, source selection, and relation definitions. |
| Approach: | They propose a parallel Russian annotation for a large and diverse English GUM RST corpus. |
| Outcome: | The proposed RST parser achieves state-of-the-art results on English and Russian corpus . it demonstrates effectiveness in monolingual and bilingual settings, transferring even with limited second-language annotation. |
Small but Mighty: New Benchmarks for Split and Rephrase (2020.emnlp-main)
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| Challenge: | Split and Rephrase is a text simplification task that requires a strong evaluation benchmark and metric . despite its relatively new nature, the benchmark dataset contains easily exploitable syntactic cues . |
| Approach: | They propose to use crowdsourced datasets to evaluate split and rephrase models . they find that the widely used benchmark dataset universally contains exploitable syntactic cues . |
| Outcome: | The proposed model performs better than the state-of-the-art model, the authors say . they show that the datasets contain significantly more diverse syntax . |
Can we obtain significant success in RST discourse parsing by using Large Language Models? (2024.eacl-long)
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| Challenge: | Experimental results show that LLMs with tens of billion parameters can perform discourse parsing tasks. |
| Approach: | They employ Llama 2 and fine-tune it with QLoRA to achieve similar results . they show that LLMs with tens of billion parameters can perform a wide range of NLP tasks . |
| Outcome: | The proposed model performs better than existing models on three benchmark datasets. |
Unsupervised Parsing by Searching for Frequent Word Sequences among Sentences with Equivalent Predicate-Argument Structures (2024.findings-acl)
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| Challenge: | Unsupervised constituency parsing focuses on identifying word sequences that form a syntactic unit (i.e., constituents) in target sentences. |
| Approach: | They propose a frequency-based parser that computes the span-overlap score as the word sequence’s frequency in the PAS-equivalent sentence set and identifies the constituent structure by finding a constituent tree with the maximum span- overlap score. |
| Outcome: | The proposed method outperforms existing unsupervised parsers in eight out of ten languages and is more accurate than previous methods. |
A Conditional Splitting Framework for Efficient Constituency Parsing (2021.acl-long)
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| Challenge: | Developing efficient and effective parsing solutions has always been a key focus in NLP. |
| Approach: | They propose a generic seq2seq parsing framework that casts constituency parsers into a series of conditional splitting decisions. |
| Outcome: | The proposed framework outperforms state-of-the-art (SoTA) methods in discourse parsing . it is based on a syntactic and discourse parsed model and is linear in number of nodes . |