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Status: Unchecked ×Keyword: BART ×Clear all

4 claims from 2 papers

1. Computer Science › Topic Modeling

   BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

   Lewis, Liu, Goyal et al. · Annual Meeting of the Association for Computational Linguistics (ACL) · 2020

   The paper presents BART, a denoising sequence-to-sequence pretraining model, tests different text-corruption methods, and reports results on generation, comprehension and translation tasks.

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   1. UncheckedBART matches RoBERTa on GLUE and SQuAD with similar training resources, and sets new best results on several generation tasks, by up to 6 ROUGE.“It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE.”
   2. UncheckedBART is reported to beat a back-translation machine translation system by 1.1 BLEU points while pretraining only on the target language.“BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining.”

2. Computer Science › Topic Modeling

   Muppet: Massive Multi-task Representations with Pre-Finetuning

   Aghajanyan, Gupta, Shrivastava, Chen, Zettlemoyer and Gupta · arXiv (Cornell University) · 2021

   The authors propose pre-finetuning, a large multi-task training stage between pre-training and fine-tuning, and report that it improves models, but only once enough tasks are used.

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   1. UncheckedAdding a large multi-task pre-finetuning stage improved RoBERTa and BART across many tasks and made fine-tuning need fewer labelled examples.“We show that pre-finetuning consistently improves performance for pretrained discriminators (e.g.~RoBERTa) and generation models (e.g.~BART) on a wide range of tasks (sentence prediction, commonsense reasoning, MRC, etc.), while also significantly improving sample efficiency during fine-tuning.”
   2. UncheckedPre-finetuning on only a few tasks can hurt performance, but beyond a critical point (usually above 15 tasks) gains grow linearly with the number of tasks.“We also show that large-scale multi-tasking is crucial; pre-finetuning can hurt performance when few tasks are used up until a critical point (usually above 15) after which performance improves linearly in the number of tasks.”