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IEEE Workshop on Spoken Language Technology (SLT)

作者： Tjandra, Andros Sakti, Sakriani Nakamura, Satoshi Nara Inst Sci & Technol Nara Japan RIKEN Ctr AIP Tokyo Japan

ISBN: (纸本)9781538643341

A sequence-to-sequence model is a neural network module for mapping two sequences of different lengths. The sequence-to-sequence model has three core modules: encoder, decoder, and attention. Attention is the bridge that connects the encoder and decoder modules and improves model performance in many tasks. In this paper, we propose two ideas to improve sequence-to-sequence model performance by enhancing the attention module. First, we maintain the history of the location and the expected context from several previous time-steps. Second, we apply multiscale convolution from several previous attention vectors to the current decoder state. We utilized our proposed framework for sequence-to-sequence speech recognition and text-to-speech systems. The results reveal that our proposed extension can improve performance significantly compared to a standard attention baseline.

关键词： sequence-to-sequence model attention mechanism multiscale alignment contextual history ASR and TTS

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A sequence-to-sequence model for Large-scale Chinese Abbreviation Database Construction 22

A Sequence-to-Sequence Model for Large-scale Chinese Abbrevi...

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15th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Wang, Chao Liu, Jingping Zhuang, Tianyi Li, Jiahang Liu, Juntao Xiao, Yanghua Wang, Wei Xie, Rui Fudan Univ Sch Comp Sci Shanghai Key Lab Data Sci Shanghai Peoples R China Fudan Aishu Cognit Intelligence Joint Res Ctr Shanghai Peoples R China East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai Peoples R China Meituan Shanghai Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781450391320

Abbreviations often used in our daily communication play an important role in natural language processing. Most of the existing studies regard the Chinese abbreviation prediction as a sequence labeling problem. However, sequence labeling models usually ignore label dependencies in the process of abbreviation prediction, and the label prediction of each character should be conditioned on its previous labels. In this paper, we propose to formalize the Chinese abbreviation prediction task as a sequence generation problem, and a novel sequence-to-sequence model is designed. To boost the performance of our deep model, we further propose a multi-level pre-trained model that incorporates character, word, and concept-level embeddings. To evaluate our methods, a new dataset for Chinese abbreviation prediction is automatically built, which contains 81,351 pairs of full forms and abbreviations. Finally, we conduct extensive experiments on a public dataset and the built dataset, and the experimental results on both datasets show that our model outperforms the state-of-the-art methods. More importantly, we build a large-scale database for a specific domain, i.e., life services in Meituan Inc., with high accuracy of about 82.7%, which contains 4,134,142 pairs of full forms and abbreviations. The online A/B testing on Meituan APP and Dianping APP suggests that Click-Through Rate increases by 0.59% and 0.86% respectively when the built database is used in the searching system. We have released our API on http://***/ddemos/abbr/ with over 87k API calls in 9 months.

关键词： Chinese abbreviation sequence-to-sequence model

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Comparing Three Data Representations for Music with a sequence-to-sequence model 33rd

Comparing Three Data Representations for Music with a Sequen...

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33rd Australasian Joint Conference on Artificial Intelligence (AI)

作者： Li, Sichao Martin, Charles Patrick Australian Natl Univ Res Sch Comp Sci Canberra Australia

ISBN: (纸本)9783030649838;9783030649845

The choices of neural network model and data representation, a mapping between musical notation and input signals for a neural network, have emerged as a major challenge in creating convincing models for melody generation. Music generation can inspire creativity in artists and the general public, but choosing a proper data representation is complicated because the same musical piece can be presented in a range of expressive ways. In this paper, we compare three different data representations on the task of generating melodies with a sequence-to-sequence model, which generates melodies with flexible length, to explore how they affect the performance of generated music. These three representations are: a monophonic representation, playing one note each time, a polyphonic representation, indicating simultaneous notes and a complex polyphonic representation, expanding the polyphonic representation with dynamics. The influences of three data representations on the generated performance are compared and evaluated by mathematical analysis and human-cantered evaluation. The results show that different data representations fed into the same model endow the generated music with various features, the monophonic representation makes the music sound more melodious to humans' ears, the polyphonic representation provides expressiveness and the complex-polyphonic representation guarantees the complexity of the generated music.

关键词： Machine learning Music generation Data representation sequence-to-sequence model

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An Exploratory Study of Abstractive Text Summarization Using a sequence-to-sequence model

An Exploratory Study of Abstractive Text Summarization Using...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Kavitha, M. Akila, K. Srm Institute of Science and Technology College of Engineering and Technology Department of Cse Chennai Vadapalani India

ISBN: (纸本)9798350394580

Text summarization has evolved over a period of time in various domains and benefits most professionals and researchers. To provide salient summarization in a short span of time, various approaches to text summarization are discovered. The objective of the paper is to increase the efficiency of the model by delivering a concise and precise summary. The intention of text summarization is to provide sufficient topic coverage and readability. The paper also presents the applications and limitations of text summarization. The study utilized a sequence-to-sequence model stacked with LSTM networks for text summarization. The results of the sequence-to-sequence model are better than other conventional models. © 2023 IEEE.

关键词： Abstractive summarization Decoder Encoder Extractive summarization LSTM sequence-to-sequence model

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U-TILISE: A sequence-to-sequence model for Cloud Removal in Optical Satellite Time Series

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Stucker, Corinne Garnot, Vivien Sainte Fare Schindler, Konrad Swiss Fed Inst Technol Chair Photogrammetry & Remote Sensing CH-8093 Zurich Switzerland Univ Zurich Inst Computat Sci CH-8057 Zurich Switzerland

Satellite image time series in the optical and infrared spectrum suffer from frequent data gaps due to cloud cover, cloud shadows, and temporary sensor outages. It has been a long-standing problem of remote sensing research how to best reconstruct the missing pixel values and obtain complete, cloud-free image sequences. We approach that problem from the perspective of representation learning and develop U-TILISE, an efficient neural model that is able to implicitly capture spatio-temporal patterns of the spectral intensities, and that can therefore be trained to map a cloud-masked input sequence to a cloud-free output sequence. The model consists of a convolutional spatial encoder that maps each individual frame of the input sequence to a latent encoding;an attention-based temporal encoder that captures dependencies between those per-frame encodings and lets them exchange information along the time dimension;and a convolutional spatial decoder that decodes the latent embeddings back into multi-spectral images. We experimentally evaluate the proposed model on EarthNet2021, a dataset of Sentinel-2 time series acquired all over Europe, and demonstrate its superior ability to reconstruct the missing pixels. Compared to a standard interpolation baseline, it increases the PSNR by 1.8 dB at previously seen locations and by 1.3 dB at unseen locations.

关键词： Clouds Optical imaging Time series analysis Optical sensors Cloud computing Adaptive optics Optical reflection Cloud removal image time series reconstruction self-attention Sentinel-2 sequence-to-sequence model

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Detecting multi-stage attacks using sequence-to-sequence model

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COMPUTERS & SECURITY 2021年 105卷 102203-102203页

作者： Zhou, Peng Zhou, Gongyan Wu, Dakui Fei, Minrui Shanghai Univ Shanghai Key Lab Power Stn Automat Technol Shanghai Peoples R China

Multi-stage attack is a kind of sophisticated intrusion strategy that has been widely used for penetrating the well protected network infrastructures. To detect such attacks, state-of-theart research advocates the use of hidden markov model (HMM). However, despite the HMM can model the relationships and dependencies among different alerts and stages for detection, they cannot handle well the stage dependencies buried in a longer sequence of alerts. In this paper, we tackle the challenge of the stages' long-term dependency and propose a new detection solution using a sequence-to-sequence (seq2seq) model. The basic idea is to encode a sequence of alerts (i.e., detector's observation) into a latent feature vector using a long-short term memory (LSTM) network and then decode this vector to a sequence of predicted attacking stages with another LSTM. By the encoder-decoder collaboration, we can decouple the local constraint between the observed alerts and the potential attacking stages, and thus able to take the full knowledge of all the alerts for the detection of stages in a sequence basis. By the LSTM, we can learn to "forget" irrelevant alerts and thereby have more opportunities to "remember" the long-term dependency between different stages for our sequence detection. To evaluate our model's effectiveness, we have conducted extensive experiments using four public datasets, all of which include simulated or re-constructed samples of real-world multi-stage attacks in controlled testbeds. Our results have successfully confirmed the better detection performance of our model compared with the previous HMM solutions. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： Multi-stage attack Intrusion detection sequence-to-sequence model Encoder-decoder architecture Long-short term memory (LSTM) network

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Deep learning based sequence to sequence model for abstractive telugu text summarization

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第11期82卷 17075-17096页

作者： Babu, G. L. Anand Badugu, Srinivasu Osmania Univ Univ Coll Engn Hyderabad India Stanley Coll Engn & Technol Women Dept CSE Hyderabad India

With the emergence of deep learning, the attention of researchers has increased significantly towards abstractive text summarization approaches. Though extractive text summarization (ETS) is an important approach, the generated summaries are not always coherent. This paper mainly focuses on the abstractive text summarization (ATS) approach for Telugu language to generate coherent summary. The majority research on ATS approach is conducted in English, while no significant research in Telugu has been documented. An abstractive Telugu text summarization model based on sequence-to-sequence (seq2seq) encoder-decoder architecture is proposed in this paper. The seq2seq model is implemented with bidirectional long short-term memory (Bi-LSTM) based encoder and long short-term memory (LSTM) based decoder. The existing ATS approaches have some drawbacks such as they cannot handle out vocabulary words, attention deficiency issue arising while handling long text sequence and repetition problem. To overcome these issues, some operating mechanisms like pointer generator network, temporal attention mechanism and coverage mechanism are also integrated in the proposed model. Besides, diverse beam search decoding algorithm is also employed to increase the diversity of generated summary. Thus, the proposed seq2seq model is the combination of Bi-LSTM and LSTM based encoder-decoder, pointer generator network, temporal attention mechanism, coverage mechanism and diverse beam search decoding algorithm. The performance of the proposed work is evaluated using the ROUGE toolkit in terms of F-measure, recall and precision. The experimental results of the proposed scheme are evaluated with other existing methods to show that the proposed ATS model outperforms existing Telugu text summarization models.

关键词： sequence-to-sequence model Temporal attention mechanism Coverage mechanism Diverse beam search Pointer generator network

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Generating Sound Words from Audio Signals of Acoustic Events with sequence-to-sequence model

Generating Sound Words from Audio Signals of Acoustic Events...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shota Ikawa Kunio Kashino Graduate School of Information Science and Technology University of Tokyo

ISBN: (纸本)9781538646595

Representing various sounds in language, such as sound words, or onomatopoeias, is not only useful as an auxiliary means for automatic speech recognition, but also essential in emerging fields such as natural human-machine communication, searching audio archives for acoustic events, and abnormality detection based on sounds. This paper proposes a novel method for sound word generation from audio signals. The method is based on an end-to-end, sequence-to-sequence framework to solve the audio segmentation problem to find an appropriate segment of audio signals along time that corresponds to a sequence of phonemes, and the ambiguity problem, where multiple words may correspond to the same sound, depending on the situations or listeners. Our tests show that the method worked efficiently and achieved a 2.8% mean phoneme error rate (MPER) and a 7.2% word error rate (WER) in a sound word generation task.

关键词： Sound word onomatopoeia sequence-to-sequence model sound transcription SOUND audio signals Audio Vibration Hearing Speech recognition anomaly detection Phonemes Word Acoustics Error analysis

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AN ANALYSIS OF INCORPORATING AN EXTERNAL LANGUAGE model INTO A sequence-to-sequence model

AN ANALYSIS OF INCORPORATING AN EXTERNAL LANGUAGE MODEL INTO...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Anjuli Kannan Yonghui Wu Patrick Nguyen Tara N. Sainath Zhifeng Chen Rohit Prabhavalkar Google Inc. USA

ISBN: (数字)9781538646588

ISBN: (纸本)9781538646595

Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model component is only trained on transcribed audio-text pairs. This leads to the use of shallow fusion with an external language model at inference time. Shallow fusion refers to log-linear interpolation with a separately trained language model at each step of the beam search. In this work, we investigate the behavior of shallow fusion across a range of conditions: different types of language models, different decoding units, and different tasks. On Google Voice Search, we demonstrate that the use of shallow fusion with an neural LM with wordpieces yields a 9.1% relative word error rate reduction (WERR) over our competitive attention-based sequence-to-sequence model, obviating the need for second-pass rescoring.

关键词： EXTERNAL LANGUAGE sequence-to-sequence model WERR modelling languages directed search Fusion reactions Fusion, biological FUSION fusion (melting) Speech recognition

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Temporal Pattern Attention-Based sequence to sequence model for Multistep Individual Load Forecasting 46

Temporal Pattern Attention-Based Sequence to Sequence model ...

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46th Annual Conference of the IEEE-Industrial-Electronics-Society (IECON)

作者： Xu, Chongchong Chen, Guo Zhou, Xiaojun Cent South Univ Sch Automat Changsha Peoples R China

ISBN: (纸本)9781728154145

Load forecasting plays a critical part in grid operation and planning. In particular, the importance of multistep load forecasting for individual power customer is increasingly prominent. Due to the strong volatility of individual consumers' electricity consumption behavior, traditional machine learning methods that cannot capture time dependence are difficult to obtain good prediction results. The recurrent neural network (RNN) can capture the time correlations existing in the load data, and the sequence to sequence (Seq2Seq) model combining two RNNs of the encoder and decoder is very suitable for multistep prediction. The temporal pattern attention mechanism can further capture the periodic change pattern in historical load data, which further improves time series modeling. We combined their advantages to propose a new type of multistep individual load forecasting framework, called the temporal pattern attention based sequence to sequence (TPA-Seq2Seq) model. This model can overcome the difficulty of multi-step prediction and further capture the load change pattern. The proposed framework was tested on real residential smart meter data, the results show that the proposed model has good prediction accuracy and is well suited for longer prediction sequences.

关键词： Individual load forecasting recurrent neural network sequence-to-sequence model temporal pattern attention mechanism

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