| Challenge: | Existing corpora of original sentences and their manual simplifications are very scarce and small in size, hindering automated text simplification systems. |
| Approach: | They propose a language-independent tool for sentence alignment from parallel/comparable TS resources. |
| Outcome: | The proposed tool performs well on English and Spanish corpora and compares sentences based on their semantic overlap. |
Similar Papers
TS-ANNO: An Annotation Tool to Build, Annotate and Evaluate Text Simplification Corpora (2022.acl-demo)
Copied to clipboard
| Challenge: | Currently, high-quality corpora of this type are rare and often of comparably small size. |
| Approach: | They propose an open-source web application for automatic text simplification. |
| Outcome: | TS-ANNO can be used for i) sentence–wise alignment, ii) rating alignment pairs, w.r.t. simplification transformations, and iv) manual simplification of complex documents. |
Text Simplification from Professionally Produced Corpora (L18-1)
Copied to clipboard
| Challenge: | Existing approaches to Text Simplification rely on the Wikipedia-Simple Wikipedia parallel corpus, which is used for many tasks. |
| Approach: | They propose to use the Newsela corpus to extract 550, 644 complex-simple sentence pairs from the corpus and introduce a lexical simplifier that uses the corpu to generate candidate simplifications. |
| Outcome: | The proposed model outperforms state-of-the-art approaches and generates candidate simplifications from the newsela corpus. |
Investigating Text Simplification Evaluation (2021.findings-acl)
Copied to clipboard
| Challenge: | Existing studies show that parallel TS corpora contain inaccurate simplifications and incorrect alignments. |
| Approach: | They propose to improve the distribution of parallel text simplification corpora to build more robust TS models. |
| Outcome: | The proposed models can be improved by improving the distribution of TS datasets. |
Parallel Text Alignment and Monolingual Parallel Corpus Creation from Philosophical Texts for Text Simplification (2021.naacl-srw)
Copied to clipboard
| Challenge: | Existing methods for text simplification require a lot of annotated data, however there are few suitable tools for this task. |
| Approach: | They propose an unsupervised method for aligning text based on Doc2Vec embeddings and an alignment algorithm capable of aligning texts at different levels. |
| Outcome: | The proposed method can be used to create a monolingual parallel corpus composed of the works of early modern philosophers and their corresponding simplified versions. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
Copied to clipboard
| Challenge: | Text simplification systems are based on the quality and quantity of complex-simple sentence pairs extracted by aligning sentences between parallel articles. |
| Approach: | They propose a neural CRF alignment model which leverages the sequential nature of sentences in parallel documents and utilizes a sentence pair model to capture semantic similarity. |
| Outcome: | The proposed model outperforms previous work on monolingual sentence alignment task by more than 5 points in F1. |
An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods to build parallel sentence simplification corpora are limited . SS is used to rephrase sentences into simpler forms for those with cognitive disabilities . |
| Approach: | They propose to build SS corpora from large-scale bilingual translation corpors using a parallel approach. |
| Outcome: | The proposed method outperforms the existing methods on WikiLarge and achieves state-of-the-art results. |
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)
Copied to clipboard
| Challenge: | Semi-automated text simplification approaches can be used to simplify text faster and at a higher quality. |
| Approach: | They propose to use autocomplete to simplify medical texts using aligned English Wikipedia sentences and pretrained neural language models to analyze the additional context. |
| Outcome: | The proposed model outperforms the best individual model by 2.1% and achieves a word prediction accuracy of 64.52%. |
Building Comparable Corpora for Assessing Multi-Word Term Alignment (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing methods to extract bilingual terminologies from corpora are limited . MWTs pose serious challenges for alignment and machine translation systems . |
| Approach: | They propose an approach to build comparable corpora and bilingual term dictionaries that evaluate bilingual term alignment in comparable corpus. |
| Outcome: | The proposed method is validated on an existing dataset and manually annotated data. |
HECTOR: A Hybrid TExt SimplifiCation TOol for Raw Texts in French (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing systems for automatic text simplification (ATS) focus on lexical and syntactic transformations, but there is no end-to-end system for French. |
| Approach: | They propose to use word embeddings for lexical simplification and rule-based strategies for syntax and discourse adaptations to improve the complexity of texts. |
| Outcome: | The proposed system performs at lexical, syntactic and discourse levels according to automatic and humanevaluations. |
Lexi: A tool for adaptive, personalized text simplification (C18-1)
Copied to clipboard
| Challenge: | Existing research on text simplification has aimed to develop generic solutions . instead, we need to develop customized simplification systems for individual users . |
| Approach: | They propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source tool for personalized text simplification. |
| Outcome: | The proposed framework is based on a free open-source tool for adaptive, personalized text simplification. |