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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How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
TL;DR: The authors provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolinguistic task performance, and find that while the pretraining data size is an important factor in the downstream performance, a designated mon-olingual tokenizer plays an equally important role in downstream performance.
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SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding
Harish Tayyar Madabushi,Edward Gow-Smith,Marcos Garcia,Carolina Scarton,Marco Idiart,Aline Villavicencio +5 more
TL;DR: The shared task on Multilingual Idiomaticity Detection and Sentence Embedding is presented, which consists of a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context.
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Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction
TL;DR: By formulating EAE as a language generation task, the method effectively encodes event structures and captures the dependencies between arguments, and design language-agnostic templates to represent the event argument structures, which are compatible with any language, hence facilitating the cross-lingual transfer.
Journal ArticleDOI
Prompting Large Language Model for Machine Translation: A Case Study
TL;DR: This paper explored the use of monolingual data and the feasibility of cross-lingual, cross-domain, and sentence-to-document transfer learning in prompting, and provided an analysis on the model outputs and discuss several problems that prompting still suffers.
Proceedings ArticleDOI
The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics
Sebastian Gehrmann,Tosin P. Adewumi,Karmanya Aggarwal,Pawan Sasanka Ammanamanchi,Anuoluwapo Aremu,Antoine Bosselut,Khyathi Raghavi Chandu,Miruna-Adriana Clinciu,Dipanjan Das,Kaustubh Dhole,Wanyu Du,Esin Durmus,Ondřej Dušek,Chris Chinenye Emezue,Varun Gangal,Cristina Garbacea,Tatsunori Hashimoto,Yufang Hou,Yacine Jernite,Harsh Jhamtani,Yangfeng Ji,Shailza Jolly,Mihir Kale,Dhruv Kumar,Faisal Ladhak,Aman Madaan,Mounica Maddela,Khyati Mahajan,Saad Mahamood,Bodhisattwa Prasad Majumder,Pedro Henrique Martins,Angelina McMillan-Major,Simon Mille,Emiel van Miltenburg,Moin Nadeem,Shashi Narayan,Vitaly Nikolaev,Andre Niyongabo Rubungo,Salomey Osei,Ankur P. Parikh,Laura Perez-Beltrachini,Niranjan Ramesh Rao,Vikas Raunak,Juan Diego Rodriguez,Sashank Santhanam,João Sedoc,Thibault Sellam,Samira Shaikh,Anastasia Shimorina,Marco Antonio Sobrevilla Cabezudo,Hendrik Strobelt,Nishant Subramani,Wei Xu,Diyi Yang,Akhila Yerukola,Jiawei Zhou +55 more
TL;DR: GEM as discussed by the authors is a living benchmark for natural language generation (NLG), its Evaluation and Metrics, which provides an environment in which models can easily be applied to a wide set of tasks and in which evaluation strategies can be tested.
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