mlsanityData to 7 Oct 2026

MTG: A Benchmark Suite for Multilingual Text Generation

arXiv 2108.07140 ↗First posted 13 Aug 2021cs.CLcited by 22

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Abstract (arXiv)

We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation. It is the first-proposed multilingual multiway text generation dataset with the largest human-annotated data (400k). It includes four generation tasks (story generation, question generation, title generation and text summarization) across five languages (English, German, French, Spanish and Chinese). The multiway setup enables testing knowledge transfer capabilities for a model across languages and tasks. Using MTG, we train and analyze several popular multilingual generation models from different aspects. Our benchmark suite fosters model performance enhancement with more human-annotated parallel data. It provides comprehensive evaluations with diverse generation scenarios. Code and data are available at urlhttps://github.com/zide05/MTG.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 13 Aug 2021.

Builds on

25 of 41 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  4. 2016SQuAD: 100,000+ Questions for Machine Comprehension of Textcited by 4,115
  5. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
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  7. 2017Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answeringcited by 1,310
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  11. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  12. 2020XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalizationcited by 738
13 more
  1. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 575
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  3. 2019Massively Multilingual Neural Machine Translation in the Wild: Findings and Challengescited by 253
  4. 2021The GEM Benchmark: Natural Language Generation, its Evaluation and Metricscited by 239
  5. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 221
  6. 2020WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarizationcited by 154
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  9. 2021Contrastive Learning for Many-to-many Multilingual Neural Machine Translationcited by 108
  10. 2019NCLS: Neural Cross-Lingual Summarizationcited by 64
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Citing papers per year: 2022: 1; 2023: 7; 2024: 8; 2025: 4; 2026: 2.

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  1. 2022mGPT: Few-Shot Learners Go Multilingualcited by 141
  2. 2023LongForm: Effective Instruction Tuning with Reverse Instructionscited by 47
  3. 2023Interpretable Long-Form Legal Question Answering with Retrieval-Augmented Large Language Modelscited by 35
  4. 2024Text Embedding Inversion Security for Multilingual Language Modelscited by 26
  5. 2023Dolphin: A Challenging and Diverse Benchmark for Arabic NLGcited by 22
  6. 2023Why Does Zero-Shot Cross-Lingual Generation Fail? An Explanation and a Solutioncited by 22
  7. 2024Large Language Models Are Cross-Lingual Knowledge-Free Reasonerscited by 16
  8. 2024Model Inversion Attacks: A Survey of Approaches and Countermeasurescited by 11
  9. 2024A Practical Examination of AI-Generated Text Detectors for Large Language Modelscited by 11
  10. 2023xPQA: Cross-Lingual Product Question Answering across 12 Languagescited by 10
  11. 2025M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysiscited by 9
  12. 2025Increasing the Robustness of the Fine-tuned Multilingual Machine-Generated Text Detectorscited by 8
10 more
  1. 2024LexSumm and LexT5: Benchmarking and Modeling Legal Summarization Tasks in Englishcited by 7
  2. 2024Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysiscited by 7
  3. 2025Exploring the Multilingual NLG Evaluation Abilities of LLM-Based Evaluatorscited by 5
  4. 2024SailCompass: Towards Reproducible and Robust Evaluation for Southeast Asian Languagescited by 3
  5. 2024SpeciaLex: A Benchmark for In-Context Specialized Lexicon Learningcited by 2
  6. 2023Extrapolating Multilingual Understanding Models as Multilingual Generatorscited by 1
  7. 2023Multi-teacher Distillation for Multilingual Spelling Correctioncited by 1
  8. 2025Survey of NLU Benchmarks Diagnosing Linguistic Phenomena: Why not Standardize Diagnostics Benchmarks?cited by 1
  9. 2026The Imperfective Paradox in Large Language Modelscited by 1
  10. 2026RotMoLE: Enhancing Mixture of Low-Rank Experts through Rotational Gating Mechanismcited by 0