mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
Linting Xue,Noah Constant,Adam Roberts,Mihir Kale,Rami Al-Rfou,Aditya Siddhant,Aditya Barua,Colin Raffel +7 more
- pp 483-498
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TLDR
This paper proposed a multilingual variant of T5, mT5, which was pre-trained on a new Common Crawl-based dataset covering 101 languages and achieved state-of-the-art performance on many multilingual benchmarks.Abstract:
The recent “Text-to-Text Transfer Transformer” (T5) leveraged a unified text-to-text format and scale to attain state-of-the-art results on a wide variety of English-language NLP tasks. In this paper, we introduce mT5, a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. We detail the design and modified training of mT5 and demonstrate its state-of-the-art performance on many multilingual benchmarks. We also describe a simple technique to prevent “accidental translation” in the zero-shot setting, where a generative model chooses to (partially) translate its prediction into the wrong language. All of the code and model checkpoints used in this work are publicly available.read more
Citations
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Exploring Text-to-Text Transformers for English to Hinglish Machine Translation with Synthetic Code-Mixing
TL;DR: A dependency-free method for generating code-mixed texts from bilingual distributed representations that is competitive with (and in some cases is even superior to) several standard methods under a diverse set of conditions.
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Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual Generation
TL;DR: Experiments show that parameter-efficient prompt tuning provides gains over standard prompt tuning when transferring between less-related languages, e.g., from English to Thai, suggesting that robust zero-shot cross-lingual generation is within reach.
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Cross-Lingual Summarization via ChatGPT
TL;DR: Zhang et al. as discussed by the authors empirically use various prompts to guide ChatGPT to perform zero-shot CLS from different paradigms (i.e., end-to-end and pipeline), and provide a preliminary evaluation on its generated summaries.
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Crossmodal-3600: A Massively Multilingual Multimodal Evaluation Dataset
TL;DR: This paper presents and makes available the Crossmodal-3600 dataset, a geographically diverse set of 3600 images each of them notated with human-generated reference cap- 007 tions in 36 languages, and applies the bench mark to model selection for massively multi- lingual image captioning models.
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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models.
TL;DR: This paper provided a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolinguistic task performance, and found that while the pretraining data size is an important factor in the downstream performance of the multilingual model, a designated mon-olingual tokenizer plays an equally important role in downstream performance.
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