Lifting the Curse of Multilinguality by Pre-training Modular Transformers
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 without any additional cost in training and inference FLOPs. In contrast to prior work which learns language-specific components post-hoc, we pre-train the modules of our Cross-lingual Modular (X-Mod) models from the start. Our experiments on natural language inference, named entity recognition and question answering show that our approach not only mitigates the negative interference between languages, but also enables positive transfer, resulting in improved monolingual and cross-lingual performance. Furthermore, our approach enables adding languages post-hoc with no measurable drop in performance, no longer limiting the model usage to the set of pre-trained languages.
Code (0)
등록된 구현이 없습니다.
Tasks
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language InferenceQuestion AnsweringSimilar Papers 제목 키워드 기반
Lifting the Curse of Multilinguality by Pre-training Modular Transformers
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 mo…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Natural Language Inference+1No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement
Modular deep learning is the state-of-the-art solution for lifting the curse of multilinguality, preventing the impact of negative interference and enabling cross-lingual performance in Multilingual Pre-trained Language …
Task ArithmeticTransfer LearningModular Sentence Encoders: Separating Language Specialization from Cross-Lingual Alignment
Multilingual sentence encoders are commonly obtained by training multilingual language models to map sentences from different languages into a shared semantic space. As such, they are subject to curse of multilinguality,…
Contrastive LearningMultiple-choiceSentenceSentence Embeddings+1Multilingual Large Language Models and Curse of Multilinguality
Multilingual Large Language Models (LLMs) have gained large popularity among Natural Language Processing (NLP) researchers and practitioners. These models, trained on huge datasets, show proficiency across various langua…
DecoderXLM-RWhen Is Multilinguality a Curse? Language Modeling for 250 High- and Low-Resource Languages
Multilingual language models are widely used to extend NLP systems to low-resource languages. However, concrete evidence for the effects of multilinguality on language modeling performance in individual languages remains…
Language ModelingLanguage Modelling