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A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network

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MEASUREMENT SCIENCE AND TECHNOLOGY 2022年第8期33卷 085013-085013页

作者： Li, Jialin Chen, Renxiang Huang, Xianzhen Chongqing Jiaotong Univ Chongqing Engn Lab Transportat Engn Applicat Robo Chongqing Peoples R China Northeastern Univ Sch Mech Engn & Automat Shenyang Peoples R China

Remaining useful life (RUL) prediction methods based on deep neural networks (DNNs) have received much attention in recent years. The collected time-series signals are usually processed by the sliding time window method into several segments with the same sequence length as the input. However, the signal processing is not only time-consuming, but also relies too much on personal experience. Moreover, the length of the time window affects the prediction results and the prediction range. Obviously, it is more desirable to remove the data processing and use an entire time series signal as the input for predicting the RUL, i.e. sequence-to-sequence RUL prediction. In order to remove the shortcomings of signal processing, this paper uses a long short-term memory (LSTM) and encoding-decoding framework to construct an unsupervised sequence data processing model. Then, a temporal convolutional network, based on a convolutional neural network, is used to further process the output data of the unsupervised sequence data processing model. The proposed sequence-to-sequence RUL prediction method not only maintains the complete sequence of the data, but has a good capability for data processing. The open access C-MAPSS simulation datasets are used for validation. The validation results show that the proposed method can realize unsupervised sequence signal reconstruction. Moreover, it has better prediction results and prediction efficiency.

关键词： remaining useful life prediction sequence-to-sequence temporal convolutional network long short-term memory network

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A sequence-to-sequence based Approach For the double Transliteration of Tunisian Dialect

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Procedia Computer Science 2018年 142卷 238-245页

作者： Jihene Younes Emna Souissi Hadhemi Achour Ahmed Ferchichi Université de Tunis ISGT LR99ES04 BESTMOD 2000 Le Bardo Tunisia Université de Tunis ENSIT 1008 Montfleury Tunisia

Transliteration consists of automatically transforming a grapheme’s transcription from one writing system to another, while preserving its pronunciation. It is usually used in the context of machine translation and cross language information retrieval, mainly to deal with the issue of named entities and technical terms. In the case of some Arabic dialects, which are used on the social web in both Latin and Arabic scripts and which are still low-resource languages, transliteration is of great benefit for the automatic generation of various linguistic resources (parallel corpora and lexica), useful for their automatic processing. In this work, we focus on the Tunisian dialect transliteration. We propose a deep learning based sequence-to-sequence approach to perform a word-level transliteration of the user generated Tunisian dialect on the social web, in both Latin to Arabic and Arabic to Latin senses.

关键词： Tunsian dialect transliteration Latin transcription Arabic transcription sequence-to-sequence deep learning natural language processing

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Indonesian Chatbot of University Admission Using a Question Answering System Based on sequence-to-sequence Model

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Procedia Computer Science 2019年 157卷 367-374页

作者： Yogi Wisesa Chandra Suyanto Suyanto School of Computing Telkom University Jl. Telekomunikasi No. 01 Terusan Buah Batu Bandung 40257 West Java Indonesia

Question and Answering (QA) system is a problem in natural language processing that can be used as the system of dialogs and chatbots. It can be used as a customer service that can provide a response to the customer quickly. A QA system receives an input in the form of sentences and produces the predictive sentences that are responses to the input. Therefore, a model that can learn such conversations is needed. This research focuses on developing a chatbot based on a sequence-to-sequence model. It is trained using a data set of conversation from a university admission. Evaluation on a small dataset obtained from the Telkom University admission on Whatsapp instant messaging application shows that the model produces a quite high BLEU score of 41.04. An attention mechanism technique using the reversed sentences improves the model to gives a higher BLEU up to 44.68.

关键词： admission chatbot attention mechanism question-answering system sequence-to-sequence

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A Novel Deep-learning based Approach for Automatic Diacritization of Arabic Poems using sequence-to-sequence Model

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第1期14卷 42-46页

作者： Mahmoud, Mohamed S. Negied, Nermin Tech Univ Munich Sch Informat Munich Germany Sch Commun & Informat Engn Giza Egypt Nile Univ Sch Engn & Appl Sci Giza Egypt

Over the last 10 years, Arabic language have attracted researchers in the area of Natural Language Processing (NLP). A lot of research papers suddenly emerged in which the main work was the processing of Arabic language and its dialects too. Arabic language processing has been given a special name ANLP (Arabic Natural Language Processing). A lot of ANLP work can be found in literature including almost all NLP applications. Many researchers have been attracted also to Arabic linguistic knowledge. The work expands from Basic Language Analysis to Semantic Level Analysis. But Arabic text semantic analysis cannot be held without considering diacritization, which can greatly affect the meaning. Many Arabic texts are written without diacritization, and Diacritizing them manually is a very tiresome process that may need an expert. Automatic diacritization systems became a demand as an initial step for processing Arabic text for any Arabic Language Processing application as Arabic diacritization is very important to get a readable and understandable Arabic text. For this reason, many researchers recently worked on building systems and tools that automatically diacritize un-diacritized Arabic texts. This work presents a novel deep learning-based sequence -to-sequence model to diacritize un-diacritized Arabic poems. The proposed model was tested and achieved high diacritization accuracy rate.

关键词： Text diacritization deep learning sequence-to-sequence regex tokenization ANLP

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Comparative study of sequence-to-sequence models:From RNNs to transformers

Comparative study of sequence-to-sequence models:From RNNs t...

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作者： Jiancong Zhu The Henry Samueli School of Engineering University of California

In this comprehensive exploration of sequence-to-sequence models in Natural Language Processing(NLP),we have traced the trajectory of their evolution and *** from foundational Recurrent Neural Networks(RNNs) to the revolutionary capabilities of Long Short-Term Memory(LSTM),In this comprehensive exploration of sequence-tosequence models in Natural Language Processing(NLP),we have meticulously traced the trajectory of their evolution and impactful *** the foundational Recurrent Neural Networks(RNNs) to the revolutionary capabilities of Long Short-Term Memory(LSTM),as well as the transformative innovations brought forth by Transformers and BERT,this review eloquently highlights the monumental advancements that have fundamentally reshaped our understanding and generation of *** crux of our comparative analysis lies in its ability to spotlight the distinctive strengths and limitations inherent in each *** an intricate examination,we uncover their nuanced applications across a diverse spectrum of NLP *** noteworthy is the pivotal role played by Transformers and the transformative Bidirectional Encoder Representations from Transformers(BERT).The paper concludes with a summary and outlook of the entire paper.

关键词： sequence-to-sequence Recurrent Neural Networks LSTMs Transformers

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SPEECH-TRANSFORMER: A NO-RECURRENCE sequence-to-sequence MODEL FOR SPEECH RECOGNITION

SPEECH-TRANSFORMER: A NO-RECURRENCE SEQUENCE-TO-SEQUENCE MOD...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Linhao Dong Shuang Xu Bo Xu Institute of Automation Chinese Academy of Sciences China

ISBN: (纸本)9781538646595

Recurrent sequence-to-sequence models using encoder-decoder architecture have made great progress in speech recognition task. However, they suffer from the drawback of slow training speed because the internal recurrence limits the training parallelization. In this paper, we present the Speech-Transformer, a no-recurrence sequence-to-sequence model entirely relies on attention mechanisms to learn the positional dependencies, which can be trained faster with more efficiency. We also propose a 2D-Attention mechanism, which can jointly attend to the time and frequency axes of the 2-dimensional speech inputs, thus providing more expressive representations for the Speech-Transformer. Evaluated on the Wall Street Journal (WSJ) speech recognition dataset, our best model achieves competitive word error rate (WER) of 10.9%, while the whole training process only takes 1.2 days on 1 GPU, significantly faster than the published results of recurrent sequence-to-sequence models.

关键词： Speech Recognition sequence-to-sequence Attention Transformer

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SCALABLE SENTIMENT FOR sequence-to-sequence CHATBOT RESPONSE WITH PERFORMANCE ANALYSIS

SCALABLE SENTIMENT FOR SEQUENCE-TO-SEQUENCE CHATBOT RESPONSE...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chih-Wei Lee Yau-Shian Wang Tsung-Yuan Hsu Kuan-Yu Chen Hung-Yi Lee Lin-shan Lee Graduate Institute of Communication Engineering National Taiwan University

ISBN: (纸本)9781538646595

Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. Some research works trying to modify the sentiment of the output sequences were reported. In this paper, we propose five models to scale or adjust the sentiment of the chatbot response: persona-based model, reinforcement learning, plug and play model, sentiment transformation network and cycleGAN, all based on the conventional seq2seq model. We also develop two evaluation metrics to estimate if the responses are reasonable given the input. These metrics together with other two popularly used metrics were used to analyze the performance of the five proposed models on different aspects, and reinforcement learning and cycleGAN were shown to be very attractive. The evaluation metrics were also found to be well correlated with human evaluation.

关键词： chatbot dialogue sequence-to-sequence style-transfer response generation Metronidazole learning (artificial intelligence) Plug and play Input sequence

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TWO-STAGE PRE-TRAINING FOR sequence TO sequence SPEECH RECOGNITION

TWO-STAGE PRE-TRAINING FOR SEQUENCE TO SEQUENCE SPEECH RECOG...

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International Joint Conference on Neural Networks (IJCNN)

作者： Fan, Zhiyun Zhou, Shiyu Xu, Bo Chinese Acad Sci Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9780738133669

The attention-based encoder-decoder structure is popular in automatic speech recognition (ASR). However, it relies heavily on transcribed data. In this paper, we propose a novel pre-training strategy for the encoder-decoder sequence-to-sequence (seq2seq) model by utilizing unpaired speech and transcripts. The pre-training process consists of two stages, acoustic pre-training and linguistic pre-training. In the acoustic pre-training stage, we use a large amount of speech to pre-train the encoder by predicting masked speech feature chunks with their contexts. In the linguistic pre-training stage, we first generate synthesized speech from a large number of transcripts using a text-to-speech (TTS) system and then use the synthesized paired data to pretrain the decoder. The two-stage pre-training is conducted on the AISHELL-2 dataset, and we apply this pre-trained model to multiple subsets of AISHELL-1 and HKUST for post-training. As the size of the subset increases, we obtain relative character error rate reduction (CERR) from 38.24% to 7.88% on AISHELL-1 and from 12.00% to 1.20% on HKUST.

关键词： pre-training speech recognition encoder-decoder sequence-to-sequence

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Contextual Density Ratio for Language Model Biasing of sequence to sequence ASR Systems 22

Contextual Density Ratio for Language Model Biasing of Seque...

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Interspeech Conference

作者： Andres-Ferrer, Jesus Albesano, Dario Zhan, Puming Vozila, Paul Nuance Commun Valencia Spain Nuance Commun Turin Italy Nuance Commun Burlington MA USA

ISBN: (纸本)9781713836902

End-2-end (E2E) models have become increasingly popular in some ASR tasks because of their performance and advantages. These E2E models directly approximate the posterior distribution of tokens given the acoustic inputs. Consequently, the E2E systems implicitly define a language model (LM) over the output tokens, which makes the exploitation of independently trained language models less straightforward than in conventional ASR systems. This makes it difficult to dynamically adapt E2E ASR system to contextual profiles for better recognizing special words such as named entities. In this work, we propose a contextual density ratio approach for both training a contextual aware E2E model and adapting the language model to named entities. We apply the aforementioned technique to an E2E ASR system, which transcribes doctor and patient conversations, for better adapting the E2E system to the names in the conversations. Our proposed technique achieves a relative improvement of up to 46.5% on the names over an E2E baseline without degrading the overall recognition accuracy of the whole test set. Moreover, it also surpasses a contextual shallow fusion baseline by 22.1 % relative.

关键词： speech recognition end-to-end sequence-to-sequence language model shallow fusion density ratio

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Forecasting Building Energy Consumption with Deep Learning: A sequence to sequence Approach 4

Forecasting Building Energy Consumption with Deep Learning: ...

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IEEE International Congress on Internet of Things (IEEE ICIOT) / IEEE International Congress on Big Data (IEEE BigData Congress) held as part of IEEE World Congress on Services (IEEE SERVICES)

作者： Sehovac, Ljubisa Nesen, Cornelius Grolinger, Katarina Western Univ Dept Elect & Comp Engn London ON Canada

ISBN: (纸本)9781728127149

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time dependencies and proposes a novel energy load forecasting methodology based on sample generation and sequence-to-sequence (S2S) deep learning algorithm. The S2S architecture that is commonly used for language translation was adapted for energy load forecasting. Experiments focus on Gated Recurrent Unit (GRU) based S2S models and Long Short-Term Memory (LSTM) based S2S models. All models were trained and tested on one building-level electrical consumption dataset, with five-minute incremental data. Results showed that, on average, the GRU S2S models outperformed LSTM S2S, RNN S2S, and Deep Neural Network models, for short, medium, and long-term forecasting lengths.

关键词： Deep Learning Energy Load Forecasting Recurrent Neural Networks sequence-to-sequence Gated Recurrent Units GRU Long Short-Term memory LSTM

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