Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches

Research Article
Open access

Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches

Yunong Zhang 1*
  • 1 Xi'an University of Architecture and Technology    
  • *corresponding author zhang.yunong.0209@gmail.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/22/20231220
ACE Vol.22
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-035-6
ISBN (Online): 978-1-83558-036-3

Abstract

In today's information age, from natural language processing to audio signal processing, from time series analysis to machine translation, the application of sequence data involves various fields. Encoder-decoder models, as a powerful approach to sequence modeling, have attracted extensive attention and research. This review paper aims to explore encoder-decoder models, focusing on the principles and operational steps of recurrent neural networks (RNN) and long short-term memory (LSTM), aiming to provide researchers and practitioners with a deep understanding of the fundamentals and applications of these models Condition. Through the analysis and summary of relevant literature, this study reveals the advantages of RNN and LSTM in sequence data processing; RNN structure is simple and effective, can process sequence input and output of any length, and can capture the time dependence in sequence data. But there is a problem of gradient disappearance, which makes it difficult to deal with long-term dependencies. To solve this problem, the LSTM model introduces different gating mechanisms, which effectively solve the gradient problem while better capturing long-term dependencies. Through the review of the principles and applications of the two, it provides a useful reference for further research and application.

Keywords:

encoder-decoder model, RNN, LSTM

Zhang,Y. (2023). Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches. Applied and Computational Engineering,22,218-226.
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References

[1]. Elman J L. Finding structure in time[J]. Cognitive science, 1990, 14(2): 179-211.

[2]. Williams R J, Zipser D. Experimental analysis of the real-time recurrent learning algorithm[J]. Connection science, 1989, 1(1): 87-111.

[3]. Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.

[4]. Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

[5]. Graves A, Graves A. Supervised sequence labelling[M]. Springer Berlin Heidelberg, 2012.

[6]. Greff K, Srivastava R K, Koutník J, et al. LSTM: A search space odyssey: IEEE Transactions on Neural Networks Learning Systems[J]. 2017.

[7]. Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks[C]//International conference on machine learning. Pmlr, 2013: 1310-1318.

[8]. Sundermeyer M, Schlüter R, Ney H. LSTM neural networks for language modeling[C]//Thirteenth annual conference of the international speech communication association. 2012.

[9]. Karpathy A, Fei-Fei L. Deep visual-semantic alignments for generating image descriptions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3128-3137.

Cite this article

Zhang,Y. (2023). Encoder-decoder models in sequence-to-sequence learning: A survey of RNN and LSTM approaches. Applied and Computational Engineering,22,218-226.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.22
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Elman J L. Finding structure in time[J]. Cognitive science, 1990, 14(2): 179-211.

[2]. Williams R J, Zipser D. Experimental analysis of the real-time recurrent learning algorithm[J]. Connection science, 1989, 1(1): 87-111.

[3]. Cho K, Van Merriënboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv:1406.1078, 2014.

[4]. Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8): 1735-1780.

[5]. Graves A, Graves A. Supervised sequence labelling[M]. Springer Berlin Heidelberg, 2012.

[6]. Greff K, Srivastava R K, Koutník J, et al. LSTM: A search space odyssey: IEEE Transactions on Neural Networks Learning Systems[J]. 2017.

[7]. Pascanu R, Mikolov T, Bengio Y. On the difficulty of training recurrent neural networks[C]//International conference on machine learning. Pmlr, 2013: 1310-1318.

[8]. Sundermeyer M, Schlüter R, Ney H. LSTM neural networks for language modeling[C]//Thirteenth annual conference of the international speech communication association. 2012.

[9]. Karpathy A, Fei-Fei L. Deep visual-semantic alignments for generating image descriptions[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2015: 3128-3137.

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