| Challenge: | Neural Text Simplification (NMT) is a widely used technique in Machine Translation (NLP) |
| Approach: | They present a replication study of Exploring Neural Text Simplification Models using OpenNMT and Newsela datasets. |
| Outcome: | The proposed systems improve on the original paper by using an updated implementation of OpenNMT and the newsela corpus alongside the original Wikipedia dataset. |
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A Detailed Evaluation of Neural Sequence-to-Sequence Models for In-domain and Cross-domain Text Simplification (L18-1)
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| Challenge: | Xu et al., 2016) show that a simple neural architecture can be efficiently used for in-domain and cross-domain text simplification. |
| Approach: | They evaluate neural sequence-to-sequence models for text simplification on Wikipedia and Newsela datasets. |
| Outcome: | The proposed model can generalize across corpora and overcome challenges when tested on Wikipedia and Newsela datasets. |
Neural CRF Model for Sentence Alignment in Text Simplification (2020.acl-main)
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| 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. |
Sentence Simplification with Memory-Augmented Neural Networks (N18-2)
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| Challenge: | Sentence simplification aims to simplify the content and structure of complex sentences . prior work has focused on monolingual machine translation (MT) and tree-based MT (TBMT). |
| Approach: | They adapt an architecture with augmented memory capacities called Neural Semantic Encoders for sentence simplification. |
| Outcome: | The proposed architecture improves on different datasets and improves human judgments. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| 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) |
Gauging the Gap Between Human and Machine Text Simplification Through Analytical Evaluation of Simplification Strategies and Errors (2023.findings-eacl)
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| Challenge: | Recent studies on text simplification have focused on lexical and syntactic simplification, but few studies have attempted to assess what kind of editing operations are performed by the systems in concrete terms. |
| Approach: | They develop an analytical evaluation framework for neural text simplification systems that includes fine-grained taxonomies of simplification strategies and errors. |
| Outcome: | The framework was used to evaluate TS models produced by human editors and multiple neural TS systems and found that human editors perform deletions and local substitutions while excessively omitting important information. |
Text Simplification from Professionally Produced Corpora (L18-1)
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| 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. |
AutoMeTS: The Autocomplete for Medical Text Simplification (2020.coling-main)
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| 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%. |
How May I Help You? Using Neural Text Simplification to Improve Downstream NLP Tasks (2021.findings-emnlp)
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| Challenge: | Recent studies have focused on rule-based and neural sequence-to-sequence (seq2sequ) TS is a technique that reduces text complexity for human consumption. |
| Approach: | They evaluate two possible uses of neural TS: simplifying input texts at prediction time and augmenting training data to provide machines with additional information during training. |
| Outcome: | The proposed approach improves performance on two datasets. |
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations (P18-1)
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| Challenge: | Using neural machine translation, we generate more than 50 million sentential paraphrase pairs from a large parallel corpus. |
| Approach: | They use a dataset of more than 50 million English-English sentential paraphrase pairs to generate them automatically using neural machine translation. |
| Outcome: | The proposed dataset outperforms all supervised systems on every SemEval semantic textual similarity competition and shows how it can be used for paraphrase generation. |
Unsupervised Neural Text Simplification (P19-1)
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| Challenge: | Existing unsupervised methods for text simplification are limited to unlabeled text . paper aims to improve the performance of unsupervised systems by incorporating labeled pairs . |
| Approach: | They propose to use unlabeled text to train a neural text simplification framework . they propose to add a pair of attentional-decoders to the framework to improve performance . |
| Outcome: | The proposed model outperforms existing supervised methods on public test data. |