Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, Veselin Stoyanov
| Challenge: | Pretraining multilingual language models at scale leads to performance gains for cross-lingual transfer tasks. |
| Approach: | They present a transformer-based multilingual masked language model pre-trained on 100 languages . they show that pretraining multilingual models at scale leads to significant performance gains . |
| Outcome: | The proposed model outperforms multilingual BERT (mBERT) on cross-lingual benchmarks. |
Similar Papers
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference (2021.findings-acl)
Copied to clipboard
| Challenge: | Multilingual transformers have been shown to have remarkable transfer skills in zero-shot settings. |
| Approach: | They investigate cross-lingual transfer abilities of XLM-R for Chinese and English natural language inference using a large scale Chinese dataset. |
| Outcome: | The proposed model trains on Chinese and English natural language inference datasets. |
Multilingual Encoder Knows more than You Realize: Shared Weights Pretraining for Extremely Low-Resource Languages (2025.acl-long)
Copied to clipboard
| Challenge: | XLM-R and mBART have advanced multilingualism in NLP, but low-resource languages such as Tibetan, Uyghur, Kazakh, and Mongolian are underserved. |
| Approach: | They propose a framework for adapting multilingual encoders to text generation in extremely low-resource languages by reusing the weights between the encoder and the decoder. |
| Outcome: | The proposed framework performs better on various downstream tasks even when compared with much larger models. |
Emerging Cross-lingual Structure in Pretrained Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work has shown that multilingual pretraining works, but is unable to measure these effects. |
| Approach: | They propose to use multilingual masked language modeling to train a model on concatenated text from multiple languages to find universal latent symmetries in embedding spaces. |
| Outcome: | The proposed models can be trained on concatenated text from multiple languages without shared vocabulary or domain similarity. |
On the Cross-lingual Transferability of Monolingual Representations (2020.acl-main)
Copied to clipboard
| Challenge: | State-of-the-art unsupervised multilingual models generalize in zero-shot cross-lingual setting . generalization ability attributed to shared subword vocabulary and joint training across multiple languages . |
| Approach: | They propose an approach that transfers a monolingual model to new languages at the lexical level. |
| Outcome: | The proposed approach is competitive with multilingual BERT on cross-lingual classification benchmarks and on a new cross-linguistic question answering dataset. |
Cross-lingual Transfer of Monolingual Models (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on cross-lingual learning using multilingual models cast doubt on shared vocabulary and joint pre-training . et al. (2005) show that model knowledge learned in the source language enhances the learning of the target language independently of language proximity. |
| Approach: | They propose a method for transferring monolingual models to other languages through continuous pre-training and investigate their results in English. |
| Outcome: | The proposed method outperforms a model trained from scratch in the GLUE benchmark for English . it shows that model knowledge from the source language enhances the learning of syntactic and semantic knowledge in english. |
Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure (2022.acl-long)
Copied to clipboard
| Challenge: | Multilingual pre-trained language models have shown impressive cross-lingual ability. |
| Approach: | They argue that cross-language ability comes from commonality between languages . they create an artificial language by modifying property in source language . |
| Outcome: | The proposed model can be implemented in multilingual and low-resource language scenarios without cross-lingual supervision or aligned data. |
Analyzing the Mono- and Cross-Lingual Pretraining Dynamics of Multilingual Language Models (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on multilingual models have focused on their cross-lingual transfer behavior . a recent study examined multilingual model learning from the multilingual pretraining signal . |
| Approach: | They analyze checkpoints during multilingual pretraining to identify when models acquire in-language and cross-lingual abilities. |
| Outcome: | The proposed model achieves high in-language performance early on, with lower-level linguistic skills acquired before more complex ones. |
Cross-lingual Visual Pre-training for Multimodal Machine Translation (2021.eacl-main)
Copied to clipboard
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. |
| Approach: | They propose to combine cross-lingual and visual pre-training to learn visually-grounded cross-linguistic representations using masked region classification and three-way parallel vision & language corpora. |
| Outcome: | The proposed models obtain state-of-the-art performance when fine-tuned for multimodal machine translation. |
Unifying Cross-Lingual Transfer across Scenarios of Resource Scarcity (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing approaches to deal with resource scarcity have not been developed to deal effectively with the problem. |
| Approach: | They propose to use a set of tools to harness data from one or more high-resource "source" languages to compensate for a shortage of data in low-resourced "target" languages. |
| Outcome: | The proposed technique can be easily adapted to unseen languages, extending the range of the proposed technique and translation-based transfer more broadly. |
Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages (2020.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies show that results from high-resource languages cannot be easily transferred to realistic, low-resourced scenarios. |
| Approach: | They analyse performance of multilingual transformer models using available resources for Hausa, isiXhosa and NER and topic classification. |
| Outcome: | The proposed models can achieve with as little as 10 or 100 labeled sentences the same performance as baselines with much more supervised training data. |