ABCD: A Graph Framework to Convert Complex Sentences to a Covering Set of Simple Sentences (2021.acl-long)
Copied to clipboard
| Challenge: | Existing work relies on rule-based methods dependent on parsing to identify atomic sentences. |
| Approach: | They propose a task to decompose complex sentences into simple ones . they propose atomic clauses as atomic sentences, and a graph edit task to predict edits . |
| Outcome: | The proposed model performs better than baselines on MinWiki and DeSSE. |
Similar Papers
Transforming Complex Sentences into a Semantic Hierarchy (P19-1)
Copied to clipboard
| Challenge: | Existing approaches for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences are lacking. |
| Approach: | They propose a method for recursively splitting and rephrasing complex English sentences into a semantic hierarchy of simplified sentences. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches in MT and information extraction tasks. |
Automated Knowledge Graph Construction using Large Language Models and Sentence Complexity Modelling (2025.emnlp-main)
Copied to clipboard
Sydney Anuyah, Mehedi Mahmud Kaushik, Sri Rama Krishna Reddy Dwarampudi, Rakesh Shiradkar, Arjan Durresi, Sunandan Chakraborty
| Challenge: | integrating coreference and decomposition increases recall on rare relations by over 20%. |
| Approach: | They propose an open-source pipeline for extracting sentence-level knowledge graphs by combining robust coreference resolution with syntactic sentence decomposition. |
| Outcome: | The proposed pipeline achieves a 99.8% exact-match accuracy on sentence simplification. |
Split and Rephrase: Better Evaluation and Stronger Baselines (P18-2)
Copied to clipboard
| 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. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
Copied to clipboard
| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
Copied to clipboard
Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Crowdsourced Corpus of Sentence Simplification with Core Vocabulary (L18-1)
Copied to clipboard
| Challenge: | a crowdsourced corpus of simplified sentences is used to generate complex sentences from more complex ones. |
| Approach: | They propose to use crowdsourced data set of simplified sentences from Japanese textbooks and reference books to generate simplified sentences. |
| Outcome: | The proposed set of simplified sentences is a good quality data set for machine learning. |
Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network (P19-1)
Copied to clipboard
| Challenge: | Existing methods for inter-sentence relation extraction do not fully exploit such dependencies. |
| Approach: | They propose a model that captures local and non-local dependencies using multi-instance learning and bi-affine pairwise scoring to predict the relation of an entity pair. |
| Outcome: | The proposed model performs comparable to state-of-the-art models on biochemistry datasets. |
Iterative Edit-Based Unsupervised Sentence Simplification (2020.acl-main)
Copied to clipboard
| Challenge: | Sentence simplification is relevant in various real-world and downstream applications. |
| Approach: | They propose an edit-based approach to unsupervised sentence simplification that uses a scoring function to score fluency, simplicity, and meaning preservation to perform edits. |
| Outcome: | The proposed model is more controllable and interpretable than state-of-the-art models on newsela and WikiLarge datasets. |
Simpler but More Accurate Semantic Dependency Parsing (P18-2)
Copied to clipboard
| Challenge: | Syntactic dependency parsing is the most popular method for automatically extracting low-level relationships between words in a sentence. |
| Approach: | They extend a syntactic dependency parser to train on and generate graph-structured representations that capture between-word relationships that are more closely related to the meaning of a sentence. |
| Outcome: | The proposed system beats the current state-of-the-art system by 0.6% and linguistically richer representations push the margin even higher. |