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Journal articles

Academic literature on the topic 'Encoder-Decoder Models'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Encoder-Decoder Models"

1

Zhang, Wenbo, Xiao Li, Yating Yang, Rui Dong, and Gongxu Luo. "Keeping Models Consistent between Pretraining and Translation for Low-Resource Neural Machine Translation." Future Internet 12, no. 12 (2020): 215. http://dx.doi.org/10.3390/fi12120215.

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Recently, the pretraining of models has been successfully applied to unsupervised and semi-supervised neural machine translation. A cross-lingual language model uses a pretrained masked language model to initialize the encoder and decoder of the translation model, which greatly improves the translation quality. However, because of a mismatch in the number of layers, the pretrained model can only initialize part of the decoder’s parameters. In this paper, we use a layer-wise coordination transformer and a consistent pretraining translation transformer instead of a vanilla transformer as the tra

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2

Lamar, Annie K. "Generating Metrically Accurate Homeric Poetry with Recurrent Neural Networks." International Journal of Transdisciplinary Artificial Intelligence 2, no. 1 (2020): 1–25. http://dx.doi.org/10.35708/tai1869-126247.

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We investigate the generation of metrically accurate Homeric poetry using recurrent neural networks (RNN). We assess two models: a basic encoder-decoder RNN and the hierarchical recurrent encoderdecoder model (HRED). We assess the quality of the generated lines of poetry using quantitative metrical analysis and expert evaluation. This evaluation reveals that while the basic encoder-decoder is able to capture complex poetic meter, it under performs in terms of semantic coherence. The HRED model, however, produces more semantically coherent lines of poetry but is unable to capture the meter. Our

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3

Markovnikov, Nikita, and Irina Kipyatkova. "Encoder-decoder models for recognition of Russian speech." Information and Control Systems, no. 4 (October 4, 2019): 45–53. http://dx.doi.org/10.31799/1684-8853-2019-4-45-53.

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Problem: Classical systems of automatic speech recognition are traditionally built using an acoustic model based on hidden Markovmodels and a statistical language model. Such systems demonstrate high recognition accuracy, but consist of several independentcomplex parts, which can cause problems when building models. Recently, an end-to-end recognition method has been spread, usingdeep artificial neural networks. This approach makes it easy to implement models using just one neural network. End-to-end modelsoften demonstrate better performance in terms of speed and accuracy of speech recognitio

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Meng, Zhaorui, and Xianze Xu. "A Hybrid Short-Term Load Forecasting Framework with an Attention-Based Encoder–Decoder Network Based on Seasonal and Trend Adjustment." Energies 12, no. 24 (2019): 4612. http://dx.doi.org/10.3390/en12244612.

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Accurate electrical load forecasting plays an important role in power system operation. An effective load forecasting approach can improve the operation efficiency of a power system. This paper proposes the seasonal and trend adjustment attention encoder–decoder (STA–AED), a hybrid short-term load forecasting approach based on a multi-head attention encoder–decoder module with seasonal and trend adjustment. A seasonal and trend decomposing technique is used to preprocess the original electrical load data. Each decomposed datum is regressed to predict the future electric load value by utilizing

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5

Dabre, Raj, and Atsushi Fujita. "Recurrent Stacking of Layers for Compact Neural Machine Translation Models." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6292–99. http://dx.doi.org/10.1609/aaai.v33i01.33016292.

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In encoder-decoder based sequence-to-sequence modeling, the most common practice is to stack a number of recurrent, convolutional, or feed-forward layers in the encoder and decoder. While the addition of each new layer improves the sequence generation quality, this also leads to a significant increase in the number of parameters. In this paper, we propose to share parameters across all layers thereby leading to a recurrently stacked sequence-to-sequence model. We report on an extensive case study on neural machine translation (NMT) using our proposed method, experimenting with a variety of dat

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Oh, Jiun, and Yong-Suk Choi. "Reusing Monolingual Pre-Trained Models by Cross-Connecting Seq2seq Models for Machine Translation." Applied Sciences 11, no. 18 (2021): 8737. http://dx.doi.org/10.3390/app11188737.

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This work uses sequence-to-sequence (seq2seq) models pre-trained on monolingual corpora for machine translation. We pre-train two seq2seq models with monolingual corpora for the source and target languages, then combine the encoder of the source language model and the decoder of the target language model, i.e., the cross-connection. We add an intermediate layer between the pre-trained encoder and the decoder to help the mapping of each other since the modules are pre-trained completely independently. These monolingual pre-trained models can work as a multilingual pre-trained model because one

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7

Khanh, Trinh Le Ba, Duy-Phuong Dao, Ngoc-Huynh Ho, et al. "Enhancing U-Net with Spatial-Channel Attention Gate for Abnormal Tissue Segmentation in Medical Imaging." Applied Sciences 10, no. 17 (2020): 5729. http://dx.doi.org/10.3390/app10175729.

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In recent years, deep learning has dominated medical image segmentation. Encoder-decoder architectures, such as U-Net, can be used in state-of-the-art models with powerful designs that are achieved by implementing skip connections that propagate local information from an encoder path to a decoder path to retrieve detailed spatial information lost by pooling operations. Despite their effectiveness for segmentation, these naïve skip connections still have some disadvantages. First, multi-scale skip connections tend to use unnecessary information and computational sources, where likable low-level

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Zheng, Chuanpan, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. "GMAN: A Graph Multi-Attention Network for Traffic Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1234–41. http://dx.doi.org/10.1609/aaai.v34i01.5477.

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Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input

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9

Monteiro, João, Bruno Martins, Miguel Costa, and João M. Pires. "Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional Models." ISPRS International Journal of Geo-Information 10, no. 9 (2021): 619. http://dx.doi.org/10.3390/ijgi10090619.

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Datasets collecting demographic and socio-economic statistics are widely available. Still, the data are often only released for highly aggregated geospatial areas, which can mask important local hotspots. When conducting spatial analysis, one often needs to disaggregate the source data, transforming the statistics reported for a set of source zones into values for a set of target zones, with a different geometry and a higher spatial resolution. This article reports on a novel dasymetric disaggregation method that uses encoder–decoder convolutional neural networks, similar to those adopted in i

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Özkaya Eren, Ayşegül, and Mustafa Sert. "Audio Captioning with Composition of Acoustic and Semantic Information." International Journal of Semantic Computing 15, no. 02 (2021): 143–60. http://dx.doi.org/10.1142/s1793351x21400018.

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Generating audio captions is a new research area that combines audio and natural language processing to create meaningful textual descriptions for audio clips. To address this problem, previous studies mostly use the encoder–decoder-based models without considering semantic information. To fill this gap, we present a novel encoder–decoder architecture using bi-directional Gated Recurrent Units (BiGRU) with audio and semantic embeddings. We extract semantic embedding by obtaining subjects and verbs from the audio clip captions and combine these embedding with audio embedding to feed the BiGRU-b

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