mlsanityData to 7 Oct 2026

Controlling Text Complexity in Neural Machine Translation

arXiv 1911.00835 ↗First posted 3 Nov 2019cs.CLcited by 32

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Abstract (arXiv)

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles. The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that these multi-task models outperform pipeline approaches that translate and simplify text independently.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 7; 2024: 8; 2025: 5; 2026: 2.

32 papers.

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  1. 2021XFORMAL: A Benchmark for Multilingual Formality Style Transfercited by 51
  2. 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
  3. 2021Trends, Limitations and Open Challenges in Automatic Readability Assessment Researchcited by 28
  4. 2022CoCoA-MT: A Dataset and Benchmark for Contrastive Controlled MT with Application to Formalitycited by 24
  5. 2023Generating Summaries with Controllable Readability Levelscited by 24
  6. 2022An Imitation Learning Curriculum for Text Editing with Non-Autoregressive Modelscited by 20
  7. 2023ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessmentcited by 19
  8. 2021Machine Translation Verbosity Control for Automatic Dubbingcited by 18
  9. 2021Isometric MT: Neural Machine Translation for Automatic Dubbingcited by 18
  10. 2024From Tarzan to Tolkien: Controlling the Language Proficiency Level of LLMs for Content Generationcited by 15
  11. 2022A Neural Pairwise Ranking Model for Readability Assessmentcited by 14
  12. 2022Controlling Translation Formality Using Pre-trained Multilingual Language Modelscited by 14
20 more
  1. 2023Controlling Pre-trained Language Models for Grade-Specific Text Simplificationcited by 14
  2. 2023Multilingual Simplification of Medical Textscited by 13
  3. 2023Learning to Paraphrase Sentences to Different Complexity Levelscited by 11
  4. 2024ReadCtrl: Personalizing text generation with readability-controlled instruction learningcited by 10
  5. 2023Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Modelcited by 9
  6. 2024Standardize: Aligning Language Models with Expert-Defined Standards for Content Generationcited by 7
  7. 2024Exploiting Dialect Identification in Automatic Dialectal Text Normalizationcited by 7
  8. 2025An Interdisciplinary Approach to Human-Centered Machine Translationcited by 6
  9. 2024Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMscited by 5
  10. 2022Uniform Complexity for Text Generationcited by 4
  11. 2024Label Confidence Weighted Learning for Target-level Sentence Simplificationcited by 3
  12. 2024SiTSE: Sinhala Text Simplification Dataset and Evaluationcited by 3
  13. 2024Strategies for Arabic Readability Modelingcited by 2
  14. 2025Treasure Hunt: Real-time Targeting of the Long Tail using Training-Time Markerscited by 2
  15. 2025Limited Preference Data? Learning Better Reward Model with Latent Space Synthesiscited by 2
  16. 2026Beyond "To whom it may concern": Tailoring Machine Translation to Audience and Intentcited by 1
  17. 2023How-to Guides for Specific Audiences: A Corpus and Initial Findingscited by 0
  18. 2025Lexical Substitution is not Synonym Substitution: On the Importance of Producing Contextually Relevant Word Substitutescited by 0
  19. 2025Toward Machine Interpreting: Lessons from Human Interpreting Studiescited by 0
  20. 2026ComplexityMT: Benchmarking the Interaction Between Text Complexity and Machine Translationcited by 0