@inproceedings{pruksachatkun-etal-2020-intermediate,
title = "Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?",
author = "Pruksachatkun, Yada and
Phang, Jason and
Liu, Haokun and
Htut, Phu Mon and
Zhang, Xiaoyi and
Pang, Richard Yuanzhe and
Vania, Clara and
Kann, Katharina and
Bowman, Samuel R.",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.467",
doi = "10.18653/v1/2020.acl-main.467",
pages = "5231--5247",
abstract = "While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.",
}
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<abstract>While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.</abstract>
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%0 Conference Proceedings
%T Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
%A Pruksachatkun, Yada
%A Phang, Jason
%A Liu, Haokun
%A Htut, Phu Mon
%A Zhang, Xiaoyi
%A Pang, Richard Yuanzhe
%A Vania, Clara
%A Kann, Katharina
%A Bowman, Samuel R.
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F pruksachatkun-etal-2020-intermediate
%X While pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, before fine-tuning it on a target task. However, it is still poorly understood when and why intermediate-task training is beneficial for a given target task. To investigate this, we perform a large-scale study on the pretrained RoBERTa model with 110 intermediate-target task combinations. We further evaluate all trained models with 25 probing tasks meant to reveal the specific skills that drive transfer. We observe that intermediate tasks requiring high-level inference and reasoning abilities tend to work best. We also observe that target task performance is strongly correlated with higher-level abilities such as coreference resolution. However, we fail to observe more granular correlations between probing and target task performance, highlighting the need for further work on broad-coverage probing benchmarks. We also observe evidence that the forgetting of knowledge learned during pretraining may limit our analysis, highlighting the need for further work on transfer learning methods in these settings.
%R 10.18653/v1/2020.acl-main.467
%U https://aclanthology.org/2020.acl-main.467
%U https://doi.org/10.18653/v1/2020.acl-main.467
%P 5231-5247
Markdown (Informal)
[Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?](https://aclanthology.org/2020.acl-main.467) (Pruksachatkun et al., ACL 2020)
ACL
- Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics.