# MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs

arXiv: https://arxiv.org/abs/2104.08692 · HTML: https://mlsanity.com/paper/2104.08692/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 49 references that are in the map.
- First posted on arXiv 18 Apr 2021.

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018)
- [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://mlsanity.com/paper/1911.00359/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training](https://mlsanity.com/paper/2007.07834/) (2020)
- [WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia](https://mlsanity.com/paper/1907.05791/) (2019)
- [UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training](https://mlsanity.com/paper/2002.12804/) (2020)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [SimAlign: High Quality Word Alignments without Parallel Training Data using Static and Contextualized Embeddings](https://mlsanity.com/paper/2004.08728/) (2020)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [Multilingual Alignment of Contextual Word Representations](https://mlsanity.com/paper/2002.03518/) (2020)

## Built on by (most cited first, 30 of 41)

- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021)
- [XLM-E: Cross-lingual Language Model Pre-training via ELECTRA](https://mlsanity.com/paper/2106.16138/) (2021)
- [DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders](https://mlsanity.com/paper/2106.13736/) (2021)
- [Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment](https://mlsanity.com/paper/2106.06381/) (2021)
- [CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs](https://mlsanity.com/paper/2112.08804/) (2021)
- [A Survey on Cross-Lingual Summarization](https://mlsanity.com/paper/2203.12515/) (2022)
- [Large Language Models for UAVs: Current State and Pathways to the Future](https://mlsanity.com/paper/2405.01745/) (2024)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training](https://mlsanity.com/paper/2109.07306/) (2021)
- [PARADISE: Exploiting Parallel Data for Multilingual Sequence-to-Sequence Pretraining](https://mlsanity.com/paper/2108.01887/) (2021)
- [GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator](https://mlsanity.com/paper/2212.10218/) (2022)
- [Zero-Shot Cross-Lingual Summarization via Large Language Models](https://mlsanity.com/paper/2302.14229/) (2023)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [DOCmT5: Document-Level Pretraining of Multilingual Language Models](https://mlsanity.com/paper/2112.08709/) (2021)
- [Cross-Lingual Supervision improves Large Language Models Pre-training](https://mlsanity.com/paper/2305.11778/) (2023)
- [Just Go Parallel: Improving the Multilingual Capabilities of Large Language Models](https://mlsanity.com/paper/2506.13044/) (2025)
- [Towards Effective Ancient Chinese Translation: Dataset, Model, and Evaluation](https://mlsanity.com/paper/2308.00240/) (2023)
- [Polyglot Prompt: Multilingual Multitask PrompTraining](https://mlsanity.com/paper/2204.14264/) (2022)
- [Prompt Learning to Mitigate Catastrophic Forgetting in Cross-lingual Transfer for Open-domain Dialogue Generation](https://mlsanity.com/paper/2305.07393/) (2023)
- [The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation](https://mlsanity.com/paper/2305.09652/) (2023)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [Event Extraction: A Survey](https://mlsanity.com/paper/2210.03419/) (2022)
- [OpenSeal: Good, Fast, and Cheap Construction of an Open-Source Southeast Asian LLM via Parallel Data](https://mlsanity.com/paper/2602.02266/) (2026)
- [Cross-Lingual Phrase Retrieval](https://mlsanity.com/paper/2204.08887/) (2022)
- [Advancing Multilingual Pre-training: TRIP Triangular Document-level Pre-training for Multilingual Language Models](https://mlsanity.com/paper/2212.07752/) (2022)
- [Exploiting Domain-Specific Parallel Data on Multilingual Language Models for Low-resource Language Translation](https://mlsanity.com/paper/2412.19522/) (2024)

## Abstract (arXiv)

Multilingual T5 (mT5) pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation pairs (mT6). Specifically, we explore three cross-lingual text-to-text pre-training tasks, namely, machine translation, translation pair span corruption, and translation span corruption. In addition, we propose a partially non-autoregressive objective for text-to-text pre-training. We evaluate the methods on eight multilingual benchmark datasets, including sentence classification, named entity recognition, question answering, and abstractive summarization. Experimental results show that the proposed mT6 improves cross-lingual transferability over mT5.