| Challenge: | Existing mPLMs only transfer NLU capability from source to target languages . mPMR allows direct inheritance of multilingual NLU capabilities to downstream tasks . |
| Approach: | They propose a method to guide multilingual pre-trained language models to perform natural language understanding in multiple languages. |
| Outcome: | mPMR enables multilingual pre-trained language models to perform natural language understanding (NLU) in multiple languages. |
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| Challenge: | Existing work on machine reading comprehension task is focused on English, but there are few efforts on other languages due to the lack of large-scale training data. |
| Approach: | They propose a cross-lingual machine reading comprehension task for other languages . they propose cloze-style reading comprehension and various neural network approaches . |
| Outcome: | The proposed model improves reading comprehension performance of Chinese datasets over state-of-the-art systems by a large margin over existing systems. |
mLongT5: A Multilingual and Efficient Text-To-Text Transformer for Longer Sequences (2023.findings-emnlp)
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| Challenge: | a new text-to-text transformer is suitable for multilingual inputs . many of the current models are English-only, making them inapplicable to other languages. |
| Approach: | They propose to extend a multilingual text-to-text transformer to handle long inputs . they use the mC4 dataset to pretrain the model to handle multilingual data . |
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Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
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Breaking the Script Barrier in Multilingual Pre-Trained Language Models with Transliteration-Based Post-Training Alignment (2024.findings-emnlp)
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| Challenge: | Recent mPLMs have shown impressive performance on crosslingual transfer tasks . however, the performance is often hindered when a lowresource target language is written in a different script than the high-resource source language. |
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| Outcome: | The proposed method outperforms the original model on Englishcentric transfer tasks up to 50%. |
Knowledge Enhanced Pre-training for Cross-lingual Dense Retrieval (2024.lrec-main)
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| Challenge: | Existing mPLMs neglect the importance of knowledge in cross-lingual dense retrieval. |
| Approach: | They propose a novel mPLM that leverages knowledge to learn language-agnostic semantic representations from a multilingual knowledge base and an annotation of Wiki. |
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REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models have achieved great success on Machine Reading Comprehension (MRC) however, the poor support in evidence extraction hinders them from further advancing MRC. |
| Approach: | They propose a REtrieval-based pre-training approach that strengthens evidence extraction during pre-training by inherited downstream MRC tasks. |
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mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus (2025.findings-acl)
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Matthieu Futeral, Armel Randy Zebaze, Pedro Ortiz Suarez, Julien Abadji, Rémi Lacroix, Cordelia Schmid, Rachel Bawden, Benoît Sagot
| Challenge: | Existing studies show that multimodal large language models can learn from text-image data. |
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How to Adapt Your Pretrained Multilingual Model to 1600 Languages (2021.acl-long)
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| Challenge: | Pretrained multilingual models perform best for languages seen during pretraining . methods exist to improve performance for unseen languages, but have been evaluated using amounts of raw text only available for a small fraction of the world’s languages. |
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Multilingual Denoising Pre-training for Neural Machine Translation (2020.tacl-1)
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Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer
| Challenge: | Existing approaches to pre-train models focus on only English corpora, but this is not common in machine translation. |
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| Outcome: | The proposed model can achieve significant performance gains across a wide variety of MT tasks. |
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
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Ozan Caglayan, Menekse Kuyu, Mustafa Sercan Amac, Pranava Madhyastha, Erkut Erdem, Aykut Erdem, Lucia Specia
| Challenge: | Pre-trained language models have been shown to improve performance in many natural language tasks. |
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| Outcome: | The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation. |