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Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI. A gradient normalization (GradNorm) algorithm that automatically balances training in deep multitask models by dynamically tuning gradient magnitudes is presented, showing that for various network architectures, for both regression and classification tasks, and on both synthetic and real datasets, GradNorm improves accuracy and reduces overfitting across multiple tasks. Close suggestions Search Search A New Web-Scale Question Answering Dataset for Model Pre-Training. 2021. 17 0 obj In 5we demon-strate the effectiveness of multi-task learning to be coming from the large scale of our MTL setup. Zdus tdf fiifmt ei cukta-tnsg kfnr`a`l, nmress jaiifrf`t prf-trna`a`l cftdejs as `et iukky, jea`l CZK trna`a`l e` n rn`lf ei ^N tnsgs mn` ac-, prevf tdf pfriercn`mf ei Z6 oy tnga`l njvn`tnlf, ei mress jntnsft trn`sifr. 6 0 obj Muppet: Massive Multi-task Representations with Pre-Finetuning Muppet: Finetuning 56 0 0 0.0 ( 0 ) A Aghajanyan, A Gupta, A Shrivastava, X Chen, L Zettlemoyer, S Gupta . Sonal Gupta, [Muppet: Massive Multi-task Representations with Pre-Finetuning](https://aclanthology.org/2021.emnlp-main.468) (Aghajanyan et al., EMNLP 2021). Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. <> /ProcSet [/PDF /Text]>> Massive Multi-taskFacebookMuppet: Massive Multi-task Representations with Pre-Finetuning[5] Muppet50480RoBERTaBART15 . 21 0 obj Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. Xilun Chen, It was introduced in this paper. Muppet Massive Multi-task Representations with Pre-Finetuning Authors: Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, Sonal Gupta We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Nkk, Kess smnka`l cftdejs a`trejumf n cuktapkamntavf. 7 0 obj endstream <> /ProcSet [/PDF /Text] /XObject <>>> Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. \f mnkk eur prf-, `ftu`fj cejfks CVXXFZ7 Cnssavf Cukta-tnsg, Zdreuld fxtf`savf fxpfracf`ts, wf sdew tdnt a`-, watdeut dnva`l te spfmaiy spfmam a`tfrcfjantf, a` tdf kew rfseurmf rflacf, wdfrf tdfrf as rfkntavfky, tacazntae` tfmd`aqufs te stnoakazf trna`a`l, n`j `j, at acpertn`t te usf tnsg-dftfrelf`feus ontmdfs watd. Zdas mn` a`dfrat tdf g`ewkfjlf mnp-, jnrj `f-tu`a`l ei prf-trna`fj cejfks mn` of u`-, stnokf, wdamd cny of nllrnvntfj a` eur mnsf ns, eusky. <> _fmf`t, werg dns sdew` lna`s irec `f-tu`a`l smdfcfs. We show that pre-finetuning consistently improves performance for pretrained discriminators (e.g. BART is presented, a denoising autoencoder for pretraining sequence-to-sequence models, which matches the performance of RoBERTa on GLUE and SQuAD, and achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks. MTDNN (Liu et al., 2019a) proves the efficiency of multi-task learning on top of the pretrained model and evaluates on several NLU benchmarks while not consider the crosslingual scenery. The model improves over roberta-base in a wide range of GLUE, QA tasks (details can be found in the paper). %PDF-1.3 3 0 obj ML/AI/DL research on approaches using extremely large models, datasets, or compute to reach SOTA Muppet: Massive Multi-task Representations with Pre-Finetuning IF:3 Literature Review Related Patents Related Grants Related Orgs Related Experts Details Highlight: We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, Xilun Chen, Luke Zettlemoyer, and Sonal Gupta. \f sdew stn`-, jnrj prf-trna`fj rfprfsf`tntae`s, wdf` iurtdfr, rf`fj watd prf-`ftu`a`l me`sastf`tky ac-, smdfcf ier fiifmtavf kfnr`a`l nt smnkf, wdamd, \f fxpkerf tdf fiifmts ei smnkf e` cukta-tnsg, kfnr`a`l n`j sdew tdf fxastf`mf ei mratamnk, pea`ts a` cukta-tnsg trna`a`l, ofye`j wdamd, a`mrfnsa`l tdf `ucofr ei tnsgs acprevfs lf`-, \f me`jumt n stujy surreu`ja`l tdf jntn fi-, maf`my ei stn`jnrj prf-trna`fj rfprfsf`tntae`s, pnrts. It was introduced in this paper. Zdas sfm-, tae` prfsf`ts eur prf-`ftu`a`l npprenmd tdnt kfnjs, te cerf stnokf n`j nmmurntf cukta-tnsg trna`a`l oy, a`trejuma`l `fw eptacazntae`, kess smnka`l, n`j, tnsg sncpka`l smdfcfs te onkn`mf fnmd ca`aontmds, sf`tntae`s, wf a`mkujf n vnrafty ei tnsgs nmress, jew` ei fnmd ei tdf tnsg typfs nke`l watd tdf, `ucofr ei sncpkfs usfj irec fnmd jura`l prf-, evfr :.4 supfrvasfj sncpkfs nmress : incakafs ei, typf. 10 0 obj endobj Muppet . It is found that BERT was significantly undertrained, and can match or exceed the performance of every model published after it, and the best model achieves state-of-the-art results on GLUE, RACE and SQuAD. aeus kess smnka`l tfmd`aqufs dnvf off` prepesfj, irec jy`ncam smnka`l oy a`vfrsf trna`a`l kess te, typfs ei tnsgs n`j jntnsfts, fnmd dnva`l ats ew`, eutput spnmfs, kess smnka`l ofmecfs fssf`tank te, f`surf stnokf trna`a`l. task. 564 564 444 921 722 667 667 722 611 556 722 722 333 389 722 611 889 This systematic study compares pre-training objectives, architectures, unlabeled datasets, transfer approaches, and other factors on dozens of language understanding tasks and achieves state-of-the-art results on many benchmarks covering summarization, question answering, text classification, and more. Armen Aghajanyan, et al. <> smnkf as mrumank ier fiifmtavf cukta-tnsg kfnr`a`l. XLNet is proposed, a generalized autoregressive pretraining method that enables learning bidirectional contexts by maximizing the expected likelihood over all permutations of the factorization order and overcomes the limitations of BERT thanks to its autore progressive formulation. sf`sf kess as jaiifrf`t irec oa`nry mknssamntae`). Edit social preview We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. 8 0 obj on HellaSwag. View 6 excerpts, cites results, methods and background. Stay informed on the latest trending ML papers with code, research developments, libraries, methods, and datasets. endobj The gains in smaller datasets are significant. endobj pnpfr, wf sdew tdnt cukta-tnsg supfrvasfj tu`a`l, ai je`f nt n suimaf`tky knrlf smnkf watd cn`y jai-, ifrf`t tnsgs, mn` of n` fiifmtavf sfme`j stnlf ei, tnsg-nl`estam prf-trna`a`l, rfceva`l tdf `ffj te, Cerf spfmamnkky, a` njjatae` te tdf stn`jnrj, kn`lunlf tnsgs, wf a`trejumf n `fw a`tfrcfjantf, cnssavf cukta-tnsg kfnr`a`l stfp (:.4 cakkae` tetnk, trna`a`l fxncpkfs) pfriercfj e` nreu`j 68 mknssa-, mntae`, succnrazntae`, qufstae` n`swfra`l, n`j, mecce` sf`sf rfnse`a`l tnsgs. endobj A new language representation model, BERT, designed to pre-train deep bidirectional representations from unlabeled text by jointly conditioning on both left and right context in all layers, which can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of tasks. Paper Muppet Massive Multi-task Representations with Pre-Finetuning What's wrong with the current multi-task learning? Sentence Completion /FullPage Do 2021: 5 By clicking accept or continuing to use the site, you agree to the terms outlined in our. A simplified and efficient method rooted in trust region theory that replaces previously used adversarial objectives with parametric noise (sampling from either a normal or uniform distribution), thereby discouraging representation change during fine-tuning when possible without hurting performance. <> Abstract: We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. . endobj Open navigation menu. % \f ofkafvf wf nrf, tdf rst te a`vfstalntf cukta-tnsg kfnr`a`l nt tdas. View 3 excerpts, cites results, methods and background. Ier fxncpkf, ier oa`nry mknssa-, weukj rftur` tdf sazf ei tdf vemnouknry (sa`mf, wf nvfrnlf nmress kess pfr tegf` lf`frntfj). In Marie-Francine Moens , Xuanjing Huang , Lucia Specia , Scott Wen-tau Yih , editors, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, EMNLP 2021, Virtual Event / Punta Cana, Dominican Republic, 7-11 November, 2021 . Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. 5 0 obj share 12 research 10/06/2021 Improving Fractal Pre-training The deep neural networks used in modern computer vision systems require . >-L,\\`?M. View 17 excerpts, references methods, background and results. 500 444 1000 500 500 333 1000 556 333 889 0 0 0 0 0 0 444 444 350 500] Experiments on few-shot dialogue completion, low-resource abstractive summarization, and multi-domain language modeling show improvements in adaptation time and performance over direct netuning or preparation via domain-adaptive pretraining. Cukta-tnsg kfnr`a`l dns off` n` a`mrfnsa`lky nm-, sumd ns CZ-J@@ sdew tdnt oy kfvfrnla`l cukta-, tnsg kfnr`a`l, wf mn` iurtdfr acprevf pfriercn`mf, e` sfvfrnk kn`lunlf of`mdcnrgs e` tep ei trnja-, cukta-tnsg kfnr`a`l e`tep ei knrlfr cejfks jefs, `et acprevf upe` tdf stn`jnrjazfj prf-trna`a`l /, `ftu`a`l. Muppet: Massive Multi-task Representations with Pre-Finetuning Armen Aghajanyan , Anchit Gupta , Akshat Shrivastava , Xilun Chen , Luke Zettlemoyer , Sonal Gupta Abstract We propose pre-finetuning, an additional large-scale learning stage between language model pre-training and fine-tuning. <> Anchit Gupta, Pre-finetuning is massively multi-task learning (around 50 datasets, over 4.8 million total labeled examples), and is designed to encourage learning of representations that generalize better to many different tasks. endstream Muppet: Massive Multi-task Representations with Pre-Finetuning 37 0 0 0.0 ( 0 ) epfrntfs evfr. Massive Multi-taskFacebookMuppet: Massive Multi-task Representations with Pre-Finetuning[5] Muppet50480RoBERTaBART . ON_Z) e` n wajf, `amn`tky acpreva`l sncpkf fimaf`my jura`l, durt pfriercn`mf wdf` ifw tnsgs nrf usfj up, u`tak n mratamnk pea`t (usunkky noevf >6) nitfr, as rfcnrgnokf, nt kfnst a` pnrt, juf te tdf fxmku-, savf usf ei sfki supfrvasae`, watdeut n`y cn`u-, nkrfnjy dnvf trna`a`l fxncpkfs ier rfkntfj preo-, kfcs, wdamd wf sdeukj of nokf te kfvfrnlf. <> stream endobj <> stream 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. Muppet: Massive multi-task representations with pre-finetuning. 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. Me`trnry te Z6, wf sdew tdnt a`merpernt-, a`l n sfme`jnry stnlf ei cukta-tnsg kfnr`a`l jefs, strntf tdf fiifmtavf`fss ei cukta-tnsg kfnr`a`l te of, Xrfvaeus werg dns rfpertfj caxfj rfsukts irec fx-, a`l te onkn`mf tdf kessfs irec jaiifrf`t tnsgs7 up-. Muppet: Massive Multi-task Representations with Pre-Finetuning RoBERTa base model This is a Massive Multi-task Pre-finetuned version of Roberta base. _eOF_Zn), n`j lf`frntae` cejfks (f.l. We call our pre-finetuned models MUPPET; Massive Multi-task RePresentation with PrE-fineTuning. Get Big Tall Clothing from Target to save money and time Select Same Day Delivery or Drive Up for easy contactless purchases me`sastf`tky rfquarf kfss jntn ier `f-tu`a`l. A new benchmark styled after GLUE is presented, a new set of more difficult language understanding tasks, a software toolkit, and a public leaderboard are presented. The results show that task-agnostic pretraining is sufficient for most cases which hopefully reduces the need for costly task-specific pretraining. <> endobj stream endobj Muppet: Massive Multi-task Representations with Pre-Finetuning, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, https://aclanthology.org/2021.emnlp-main.468, https://aclanthology.org/2021.emnlp-main.468.pdf, Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License, Creative Commons Attribution 4.0 International License. Papers With Code is a free resource with all data licensed under. Through extensive experiments, we show that incorporating pre-finetuning to RoBERTa [ ] and BART [ ] models yields consistent improvements, including new state-of-the-art performance for RTE [ ] and HellaSWAG [ ] <> Pre-finetuning is massively multi-task learning (around 50 datasets, over. ), while also significantly improving sample efficiency during fine-tuning. <> 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. The method is called MUPPET: Massive Multi-task RePresentation with PrE-fineTuning. Zdf kess iu`mtae`s, tdf lrnjaf`ts irec fnmd tnsg nrf nvfrnlfj ofierf, \f sdew twe strntflafs te kfnr` cukta-tnsg rfprf-, cejfk as trya`l te eptacazf `et n sa`lkf eobfm-, tavf out sfvfrnk petf`tankky mecpfta`l eobfmtavfs, te mrfntf n u`afj rfprfsf`tntae` nmress sfvfrnk, smf`t, ceva`l nke`l tdf lrnjaf`t ei n sa`lkf tnsg, cny `et of tdf eptacnk jarfmtae` ier tdf cejfk te, cevf te kfnr` n sa`lkf u`afj rfprfsf`tntae` nmress, tnsgs. Muppet: Massive Multi-task Representations with Pre-Finetuning Armen Aghajanyan , Anchit Gupta , Akshat Shrivastava , Xilun Chen , Luke Zettlemoyer , Sonal Gupta 2021, 00:00 (edited 01 Feb 2022) EMNLP (1) 2021 Readers: Everyone
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