Lifting the Curse of Multilinguality by Pre-training Modular Transformers

@inproceedings{Pfeiffer2022LiftingTC,
  title={Lifting the Curse of Multilinguality by Pre-training Modular Transformers},
  author={Jonas Pfeiffer and Naman Goyal and Xi Victoria Lin and Xian Li and James Cross and Sebastian Riedel and Mikel Artetxe},
  booktitle={NAACL},
  year={2022}
}
Multilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific modules, which allows us to grow the total capacity of the model, while keeping the total number of trainable parameters per language constant. In contrast with prior work that learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X… 
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References

SHOWING 1-10 OF 68 REFERENCES
On the Cross-lingual Transferability of Monolingual Representations
TLDR
This work designs an alternative approach that transfers a monolingual model to new languages at the lexical level and shows that it is competitive with multilingual BERT on standard cross-lingUAL classification benchmarks and on a new Cross-lingual Question Answering Dataset (XQuAD).
MAD-X: An Adapter-based Framework for Multi-task Cross-lingual Transfer
TLDR
MAD-X is proposed, an adapter-based framework that enables high portability and parameter-efficient transfer to arbitrary tasks and languages by learning modular language and task representations and introduces a novel invertible adapter architecture and a strong baseline method for adapting a pretrained multilingual model to a new language.
UNKs Everywhere: Adapting Multilingual Language Models to New Scripts
TLDR
This work proposes a series of novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts and demonstrates that they can yield improvements for low- resource languages written in scripts covered by the pretrained model.
Emerging Cross-lingual Structure in Pretrained Language Models
TLDR
It is shown that transfer is possible even when there is no shared vocabulary across the monolingual corpora and also when the text comes from very different domains, and it is strongly suggested that, much like for non-contextual word embeddings, there are universal latent symmetries in the learned embedding spaces.
From English To Foreign Languages: Transferring Pre-trained Language Models
TLDR
This work tackles the problem of transferring an existing pre-trained model from English to other languages under a limited computational budget and demonstrates that its models are better than multilingual BERT on two zero-shot tasks: natural language inference and dependency parsing.
MAD-G: Multilingual Adapter Generation for Efficient Cross-Lingual Transfer
TLDR
MAD-G (Multilingual ADapter Generation), which contextually generates language adapters from language representations based on typological features, offers substantial benefits for low-resource languages, particularly on the NER task in low- resource African languages.
Rethinking embedding coupling in pre-trained language models
TLDR
The analysis shows that larger output embeddings prevent the model's last layers from overspecializing to the pre-training task and encourage Transformer representations to be more general and more transferable to other tasks and languages.
Improving Multilingual Models with Language-Clustered Vocabularies
TLDR
This work introduces a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters, thus balancing the trade-off between cross-lingual subword sharing and language-specific vocABularies.
From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers
TLDR
It is demonstrated that the inexpensive few-shot transfer (i.e., additional fine-tuning on a few target-language instances) is surprisingly effective across the board, warranting more research efforts reaching beyond the limiting zero-shot conditions.
Orthogonal Language and Task Adapters in Zero-Shot Cross-Lingual Transfer
TLDR
This work proposes orthogonal language and task adapters (dubbed orthoadapters) for cross-lingual transfer that are trained to encode language- and task-specific information that is complementary to the knowledge already stored in the pretrained transformer's parameters.
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