Sentence Simplification by Monolingual Machine Translation

By Sander Wubben, Antal van den Bosch, Emiel Krahmer. Annual Meeting of the Association for Computational Linguistics, 2012

319 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/50dfb7358cc85cc7ab0eda68c517164ebd205d42

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Citations in the corpus

Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (2018, methodology): “Phrased-based machine translation models have had success in the ﬁelds of machine translation, style transfer (X U 12) and text simpliﬁcation (Wubben et al., 2012b; Xu et al., 2016).”
MUSS: Multilingual Unsupervised Sentence Simplification by Mining Paraphrases (2022, unknown): “Copying the Source Over Simplification systems have suffered from not modifying the source sentence enough and often fall back to keeping it entirely unchanged (Wubben et al., 2012; Martin et al., 2020).”
BLEU is Not Suitable for the Evaluation of Text Simplification (2018, methodology): “Along with the application of parallel corpora and MT techniques for TS (e.g., Zhu et al., 2010; Wubben et al., 2012; Narayan and Gardent, 2014), BLEU became the main automatic metric for TS, despite its deficiencies (see §2).”
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations (2020, methodology): “BLEU has shown positive correlation with human judgements of grammaticality and meaning preservation (Štajner et al., 2014; Wubben et al., 2012; Xu et al., 2016), while SARI has high correlation with judgements of simplicity gain (Xu et al.”
FELIX: Flexible Text Editing Through Tagging and Insertion (2020, methodology): “In Table 4 we compare against three state-of-the-art SMT based simpliﬁcation systems: (1) PBMT-R (Wubben et al., 2012), a phrase-based machine translation model.”
Deep Learning for Joint Source-Channel Coding of Text (2018, methodology): “Word error rate is commonly used to evaluate performance in speech recognition and machine translation [30, 31].”
EASSE: Easier Automatic Sentence Simplification Evaluation (2019, methodology): “For instance, we include those that rely on phrasebased statistical MT, either by itself (e.g. PBSMTR (Wubben et al., 2012)), or coupled with semantic analysis, (e.g. Hybrid (Narayan and Gardent, 2014)).”
Split and Rephrase (2017, methodology): “Finally, sentence (or text) simpliﬁcation aims to produce a text that is easier to understand (Siddharthan et al., 2004; Zhu et al., 2010; Woodsend and Lapata, 2011; Wubben et al., 2012; Narayan and Gardent, 2014; Xu et al., 2015; Narayan and Gardent, 2016; Zhang and La-pata, 2017).”
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation (2017, background): “Wubben et al. (2012), comparing text simplification strategies, found low, but significant correlations between bleu and fluency judgements, and a very low, negative correlation between bleu and adequacy.”
Problems in Current Text Simplification Research: New Data Can Help (2015, methodology): “It has become a benchmark dataset for simplification largely because of its size and availability, and because follow-up papers (Woodsend and Lapata, 2011a; Coster and Kauchak, 2011; Wubben et al., 2012; Narayan and Gardent, 2014; Siddharthan and Angrosh, 2014; Angrosh et al., 2014) often compare wi”
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks (2016, methodology): “Inspired by the recently proposed architectures for machine translation (Bahdanau et al., 2014), our model consists of a conditional recurrent neural network, which acts as a decoder to generate the summary of an input sentence, much like a standard recurrent language model.”
A Neural Attention Model for Abstractive Sentence Summarization (2015, methodology): “Wubben et al. (2012) utilize MOSES directly as a method for text simplification.”
Linguistic realisation as machine translation: Comparing different MT models for AMR-to-text generation (2017, methodology): “In addition, Pourdamghani et al. (2016) use PBMT, which is devised for translation but also utilised in other NLP tasks, e.g. text simpliﬁcation (Wubben et al., 2012; ˇStajner et al., 2015).”
Assessing Relative Sentence Complexity using an Incremental CCG Parser (2016, background): “There have been efforts to automatically simplify Wikipedia to cater its content for children and English language learners (Zhu et al., 2010; Woodsend and Lapata, 2011; Coster and Kauchak, 2011; Wubben et al., 2012; Siddharthan and Mandya, 2014).”
Optimizing Statistical Machine Translation for Text Simplification (2016, methodology): “…black box (Coster and Kauchak, 2011a,b; Narayan and Gardent, 2014; Angrosh et al., 2014; ˇStajner et al., 2015) or they have been limited to modifying only one aspect of it, such as the translation model (Zhu et al., 2010; Woodsend and Lapata, 2011) or the reranking component (Wubben et al., 2012).”