Pre-trained models that talk many languages have performed excellently on natural language interpretation challenges. Large volumes of unlabeled data in lots of of languages are sometimes used to coach these models. Although being pre-trained totally on English data, recent huge language models have remarkable multilingual abilities. All of those models, nonetheless, have one thing in common: they will only hold so many representations of various languages. In consequence, models perform badly on languages with fewer pretraining data and more pretraining languages. The “curse of multilingualism” is one other name for this. 

For existing multilingual models, natural language production tasks provide additional issues since they could overfit the training languages and partially forget their generation skill within the goal language, leading to text that has the suitable meaning but must be written accurately. The “source language hallucination problem” is how they describe this. Researchers from Google DeepMind suggest the modular multilingual T5, the primary modular multilingual generative model, to beat these two drawbacks. To spice up capability for multilingual modeling, mmT5 allots a modest variety of language-specific parameters during pretraining. 

By freezing the language-specific modules during fine-tuning and adjusting the common parameters, they allow direct adaptation to a goal language by switching to the suitable language-specific module. Additionally they note one other area for improvement with mmT5: the fine-tuned shared representations could diverge from the decoder’s frozen modular representations. Thus, the modular approach is very similar to its non-modular equivalents, susceptible to producing content in the wrong language. They suggest freezing a portion of the common decoder parameters to assist with this, which makes a major difference in zero-shot cross-lingual generation for modular generative models. 

They discover that the mmT5 model effectively addresses the 2 drawbacks of multilingual sequence-to-sequence models: 1) By allowing for more model capability to be added to numerous languages during pretraining, mmT5 alleviates the curse of multilingualism. On a typical collection of multilingual NLU and NLG tasks, it outperforms conventional baselines and mT5 at the identical parameter sizes; furthermore, mmT5 impressively addresses the source language hallucination problem on zero-shot cross-lingual text production. Based on their investigation, for a zero-shot multilingual summarization job, mT5 only produces text within the goal language 7% of the time, but mmT5 makes the text in the suitable language for 99% of cases. 

A modular multilingual encoder-decoder model called mmT5 has been suggested. The majority of mmT5 parameters used during multilingual pretraining are shared across tasks, but each language can be given a limited variety of parameters which might be exclusive to that language. They showed that adding modularity as an architectural inductive bias greatly increases training efficiency, achieving the identical perplexity as a comparable completely dense model in a fourth of the update steps. On a big selection of tasks, including Query Answering, Semantic Parsing, Summarization, and Classification in each zero-shot and multilingual contexts, mmT5 significantly outperforms comparable models. 

Finally, they reveal that the model reliably produces text within the goal language while fine-tuning mmT5 on a goal task in a source language by freezing certain decoder regions. Due to this fact, modularity eliminates source language hallucinations in cross-lingual transmission cases.

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