# mPMR: A Multilingual Pre-trained Machine Reader at Scale

arXiv: https://arxiv.org/abs/2305.13645 · HTML: https://mlsanity.com/paper/2305.13645/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 23 May 2023.

## Builds on (most cited first, 24 of 24)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [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)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [MLQA: Evaluating Cross-lingual Extractive Question Answering](https://mlsanity.com/paper/1910.07475/) (2019)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [Emerging Cross-lingual Structure in Pretrained Language Models](https://mlsanity.com/paper/1911.01464/) (2019)
- [Pre-training Tasks for Embedding-based Large-scale Retrieval](https://mlsanity.com/paper/2002.03932/) (2020)
- [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](https://mlsanity.com/paper/2205.06266/) (2022)
- [Look at the First Sentence: Position Bias in Question Answering](https://mlsanity.com/paper/2004.14602/) (2020)
- [VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2010.16046/) (2020)
- [GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2110.07679/) (2021)
- [Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model](https://mlsanity.com/paper/2011.11499/) (2020)
- [Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond](https://mlsanity.com/paper/2010.12405/) (2020)
- [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://mlsanity.com/paper/2110.08151/) (2021)
- [XLM-K: Improving Cross-Lingual Language Model Pre-training with Multilingual Knowledge](https://mlsanity.com/paper/2109.12573/) (2021)
- [Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples](https://mlsanity.com/paper/2111.10962/) (2021)
- [ConNER: Consistency Training for Cross-lingual Named Entity Recognition](https://mlsanity.com/paper/2211.09394/) (2022)
- [Multilingual AMR Parsing with Noisy Knowledge Distillation](https://mlsanity.com/paper/2109.15196/) (2021)
- [PeerDA: Data Augmentation via Modeling Peer Relation for Span Identification Tasks](https://mlsanity.com/paper/2210.08855/) (2022)
- [Retrofitting Multilingual Sentence Embeddings with Abstract Meaning Representation](https://mlsanity.com/paper/2210.09773/) (2022)

## Built on by (most cited first, 2 of 2)

- [Reasons to Reject? Aligning Language Models with Judgments](https://mlsanity.com/paper/2312.14591/) (2023)
- [Understanding LLMs' Cross-Lingual Context Retrieval: How Good It Is And Where It Comes From](https://mlsanity.com/paper/2504.10906/) (2025)

## Abstract (arXiv)

We present multilingual Pre-trained Machine Reader (mPMR), a novel method for multilingual machine reading comprehension (MRC)-style pre-training. mPMR aims to guide multilingual pre-trained language models (mPLMs) to perform natural language understanding (NLU) including both sequence classification and span extraction in multiple languages. To achieve cross-lingual generalization when only source-language fine-tuning data is available, existing mPLMs solely transfer NLU capability from a source language to target languages. In contrast, mPMR allows the direct inheritance of multilingual NLU capability from the MRC-style pre-training to downstream tasks. Therefore, mPMR acquires better NLU capability for target languages. mPMR also provides a unified solver for tackling cross-lingual span extraction and sequence classification, thereby enabling the extraction of rationales to explain the sentence-pair classification process.