@inproceedings{guo-etal-2020-continual,
title = "Continual Learning Long Short Term Memory",
author = "Guo, Xin and
Tian, Yu and
Xue, Qinghan and
Lampropoulos, Panos and
Eliuk, Steven and
Barner, Kenneth and
Wang, Xiaolong",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.164",
doi = "10.18653/v1/2020.findings-emnlp.164",
pages = "1817--1822",
abstract = "Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task{'}s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.",
}
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<abstract>Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task’s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.</abstract>
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%0 Conference Proceedings
%T Continual Learning Long Short Term Memory
%A Guo, Xin
%A Tian, Yu
%A Xue, Qinghan
%A Lampropoulos, Panos
%A Eliuk, Steven
%A Barner, Kenneth
%A Wang, Xiaolong
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F guo-etal-2020-continual
%X Catastrophic forgetting in neural networks indicates the performance decreasing of deep learning models on previous tasks while learning new tasks. To address this problem, we propose a novel Continual Learning Long Short Term Memory (CL-LSTM) cell in Recurrent Neural Network (RNN) in this paper. CL-LSTM considers not only the state of each individual task’s output gates but also the correlation of the states between tasks, so that the deep learning models can incrementally learn new tasks without catastrophically forgetting previously tasks. Experimental results demonstrate significant improvements of CL-LSTM over state-of-the-art approaches on spoken language understanding (SLU) tasks.
%R 10.18653/v1/2020.findings-emnlp.164
%U https://aclanthology.org/2020.findings-emnlp.164
%U https://doi.org/10.18653/v1/2020.findings-emnlp.164
%P 1817-1822
Markdown (Informal)
[Continual Learning Long Short Term Memory](https://aclanthology.org/2020.findings-emnlp.164) (Guo et al., Findings 2020)
ACL
- Xin Guo, Yu Tian, Qinghan Xue, Panos Lampropoulos, Steven Eliuk, Kenneth Barner, and Xiaolong Wang. 2020. Continual Learning Long Short Term Memory. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1817–1822, Online. Association for Computational Linguistics.