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Real Time Machine Translation System between Indian Languages 6

Real Time Machine Translation System between Indian Language...

引用

6th International Conference on Trends in Electronics and Informatics (ICOEI)

作者： Patil, Aarati H. Patil, Snehal S. Patil, Shubham M. Nagarhalli, Tatwadarshi P. Vidyavardhinis Coll Engn & Technol Comp Engn Vasai India

ISBN: (纸本)9781665483285;9781665483292

Language understanding is one of those perpetual challenges which has dogged research from many decades. As a means of connecting with others, but communication is not limited to a single language. India contains around 121 different languages. As a result, there is a linguistic barrier. Natural Language Processing is the process of developing a communicational interface between machines and humans. A model that turns the supplied source language text (Marathi) into the desired language text (Gujarati) using Text to Text Translation has been developed. Because LSTMs allow us with a vast variety of parameters like as learning rates, input and output biases, and there is no need for existence, this model is constructed by adding multilingual features to the LSTM model based on deep learning Principles. The goal of the proposed is to create a deep learning-based translation system. The suggested system uses a variety of models to perform text-to-text translation, after which the input text will be translated into the target language (text).

关键词： Long Short-Term Memory encoder-decoder model Neural Machine Translation Indian language

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Automatically Generating API Usage Patterns from Natural Language Queries 25

Automatically Generating API Usage Patterns from Natural Lan...

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25th Asia-Pacific Software Engineering Conference (APSEC)

作者： Tian, Yanfei Wang, Xu Sun, Hailong Zhao, Yi Guo, Chunbo Liu, Xudong Beihang Univ Sch Comp Sci & Engn SKLSDE Lab Beijing Peoples R China Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China

ISBN: (纸本)9781728119700

Automatically generating code from natural language query is a very promising but much challenging direction. Existing approaches either try to generate the whole code or only predict a small part of critical code elements such as API sequence. Meanwhile, API usage patterns, including APIs and API-related control-flow statements, have the moderate complexity, but can provide enough code framework information and are very helpful for developers to implement various functionalities. Therefore, in this work, we study the problem of generating API usage patterns, represent API usage patterns by one special constrained tree API-MCTree and design one new API-MCTree decoder for automatically transforming natural language queries to API usage patterns, which can leverage both the difference of control-flow statement types and the syntactic knowledge of API usage patterns. We evaluate our model with annotated code snippets in real Java projects collected from GitHub, and the experimental results show that our approach is effective and outperforms the related approaches.

关键词： API encoder-decoder model API Usage Pattern Code Generation

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ICAL: Implicit Character-Aided Learning for Enhanced Handwritten Mathematical Expression Recognition 18th

ICAL: Implicit Character-Aided Learning for Enhanced Handwri...

引用

18th International Conference on Document Analysis and Recognition (ICDAR)

作者： Zhu, Jianhua Gao, Liangcai Zhao, Wenqi Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9783031705489;9783031705496

Significant progress has been made in the field of handwritten mathematical expression recognition, while existing encoder-decoder methods are usually difficult to model global information in LATEX. Therefore, this paper introduces a novel approach, Implicit Character-Aided Learning (ICAL), to mine the global expression information and enhance handwritten mathematical expression recognition. Specifically, we propose the Implicit Character Construction Module (ICCM) to predict implicit character sequences and use a Fusion Module to merge the outputs of the ICCM and the decoder, thereby producing corrected predictions. By modeling and utilizing implicit character information, ICAL achieves a more accurate and context-aware interpretation of handwritten mathematical expressions. Experimental results demonstrate that ICAL notably surpasses the state-of-the-art(SOTA) models, improving the expression recognition rate (ExpRate) by 2.25%/1.81%/1.39% on the CROHME 2014/2016/2019 datasets respectively, and achieves a remarkable 69.06% on the challenging HME100k test set. We make our code available on the GitHub. (https://***/qingzhenduyu/ICAL).

关键词： handwritten mathematical expression recognition transformer implicit character-aided learning encoder-decoder model

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CoMER: modeling Coverage for Transformer-Based Handwritten Mathematical Expression Recognition 17th

CoMER: Modeling Coverage for Transformer-Based Handwritten M...

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17th European Conference on Computer Vision (ECCV)

作者： Zhao, Wenqi Gao, Liangcai Peking Univ Wangxuan Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9783031198144;9783031198151

The Transformer-based encoder-decoder architecture has recently made significant advances in recognizing handwritten mathematical expressions. However, the transformer model still suffers from the lack of coverage problem, making its expression recognition rate (ExpRate) inferior to its RNN counterpart. Coverage information, which records the alignment information of the past steps, has proven effective in the RNN models. In this paper, we propose CoMER, a model that adopts the coverage information in the transformer decoder. Specifically, we propose a novel Attention Refinement Module (ARM) to refine the attention weights with past alignment information without hurting its parallelism. Furthermore, we take coverage information to the extreme by proposing self-coverage and cross-coverage, which utilize the past alignment information from the current and previous layers. Experiments show that CoMER improves the ExpRate by 0.61%/2.09%/1.59% compared to the current state-of-the-art model, and reaches 59.33%/59.81%/62.97% on the CROHME 2014/2016/2019 test sets. (Source code is available at https://***/Green-Wood/CoMER)

关键词： Handwritten mathematical expression recognition Transformer Coverage Alignment encoder-decoder model

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Sequential Memory modelling for Video Captioning 19

Sequential Memory Modelling for Video Captioning

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19th IEEE-India-Council International Conference (INDICON)

作者： Puttaraja Nayaka, Chidambara Manikesh Sharma, Nitin Anand, Kumar M. Natl Inst Technol Karnataka Dept Informat Technol Surathkal 575025 India

ISBN: (纸本)9781665473507

In recent years, the automatic generation of natural language descriptions of video has focused on deep learning research and natural voice processing. Video understanding has multiple applications such as video search and indexing, but video subtitles are a correct sophisticated topic for complex and diverse types of video content. However, the understanding between video and natural language sets remains an open issue to better understand the video and create multiple methods to create a set automatically. The deep learning method has a major focus on the direction of video processing with performance and highspeed computing capabilities. This polling discusses an encoderdecoder network end-in-frame based on a deep learning approach to generate caption. In this paper we will describe the model, dataset and parameters used to evaluate the model.

关键词： Deep learning NLP Natural Language Processing LSTM encoder-decoder model

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Neural Morphological Segmentation model for Mongolian

Neural Morphological Segmentation Model for Mongolian

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International Joint Conference on Neural Networks (IJCNN)

作者： Wang, Weihua Fam, Rashel Bao, Feilong Lepage, Yves Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Waseda Univ Grad Sch Informat Prod & Syst Kitakyshu Japan Inner Mongolia Key Lab Mongolian Informat Proc Te Hohhot Peoples R China

ISBN: (纸本)9781728119854

Morphological segmentation is useful for processing Mongolian. In this paper, we manually build a morphological segmentation data set for Mongolian. We then present a character-based encoder-decoder model with attention mechanism to perform the morphological segmentation task. We further investigate the influence of analogy features extracted from scratch and improve the performance of our model using multi languages setting. Experimental results show that our encoder-decoder model with attention mechanism provides a strong baseline for Mongolian morphological segmentation. The analogy features provide useful information to the model and improve the performance of the system. The use of multi languages data set shows the capability of our model to acquire knowledge through different languages and delivers the best result.

关键词： Morphological Segmentation Mongolian encoder-decoder model

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Automatic Text Summarization Based on Transformer and Switchable Normalization 17

Automatic Text Summarization Based on Transformer and Switch...

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IEEE Int Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Luo, Tao Guo, Kun Guo, Hong Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Fuzhou Univ Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Peoples R China Fuzhou Univ Key Lab Spatial Data Min & Informat Sharing Minist Educ Fuzhou Peoples R China

ISBN: (纸本)9781728143286

With the development of text summarization research, the methods based on RNN with the encoder-decoder model gradually become the mainstream. However, RNN tends to forget previous context information and leads to the lack of original information. That will reduce the accuracy of the generated summarizations. The Transformer model uses the self-attention mechanism to encode and decode historical information so that it can achieve better performance than RNN in learning context information. In this paper, a text summarization model based on transformer and switchable normalization is proposed. The accuracy of the model is improved by optimizing the normalization layer. Compared with other models, the new model has a great advantage in understanding words' semantics and associations. Experimental results in English Gigaword dataset show that the proposed model can achieve high ROUGE values and make the summarization more readable.

关键词： text summarization encoder-decoder model attention transformer switchable normalization

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Visual Prompt Learning for Chinese Handwriting Recognition 18th

Visual Prompt Learning for Chinese Handwriting Recognition

引用

18th International Conference on Document Analysis and Recognition (ICDAR)

作者： Yao, Gang Ding, Ning Zhao, Tianqi Zhao, Kemeng Tang, Pei Tao, Yao Peng, Liangrui Tsinghua Univ Dept Elect Engn Beijing Peoples R China Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing Peoples R China Huawei Technol Noahs Ark Lab Shenzhen Peoples R China

ISBN: (纸本)9783031705328;9783031705335

Recognizing Chinese handwriting in unconstrained scenarios remains a challenging task due to wide variations in writing styles and imaging conditions. Recently, prompt learning in natural language processing has shown success in leveraging context awareness in various domains. This paper proposes to incorporate visual prompt learning into an encoder-decoder model for handwriting recognition. For the encoder, multi-scale meta prompts are incorporated to utilize contextual information in internal feature representations. For the decoder, additional character-level visual prompts are used along with the embeddings of previously predicted text to guide the decoding process. Experiments conducted on the SCUT-HCCDoc, SCUT-EPT and CASIA-HWDB Chinese handwriting datasets validate the effectiveness of the proposed methods.

关键词： handwriting recognition visual prompt learning encoder-decoder model

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A Novel Two-Factor Attention encoder-decoder Network through Combining Temporal and Prior Knowledge for Weather Forecasting

A Novel Two-Factor Attention Encoder-Decoder Network through...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Yuan, Minglei Ji, Xiaozhong Lu, Tong Chen, Pengfei Zhang, Hualu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing Peoples R China Nati Grp Corp Informat Technol & Commun Co Nanjing Peoples R China

ISBN: (纸本)9781728119854

This paper proposes a novel two-factor attention based encoder-decoder model (TwoFactorencoderdecoder) for multivariate weather prediction. The proposed model learns attention weights from two factors, namely, temporal information and prior knowledge inferred information. Here, temporal information contains change patterns hidden in observed time series data, while prior knowledge inferred information gives various types of meteorological observations in weather forecasting. Attention weights of the two factors are used to select the intermediate outputs of the encoder, and then combine the selected result with information inferred by prior knowledge for weather forecasting by a more effective way. In addition, this paper proposes a loss function for multivariate prediction. Compared with Mean Square Error (MSE) loss function, the proposed loss function can fit small variances more accurately in performing multivariate prediction. Compared with the attention model that only uses temporal information or the prior knowledge inferred information, the proposed TwoFactorencoderdecoder model has encouraging improvements in prediction accuracy on the public weather forecasting dataset, namely, the MAPE of t2m is increased by 5.42%, the MAPE of rh2m is increased by 2.92%, and the MAPE of w2m is increased by 1.67%, which shows the effect of the two-factor attention mechanism. Source code for the complete system will be available at https://***/YuanMLer/TFAencoderdecoder.

关键词： two-factor attention weather forecasting encoder-decoder model

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DPAST-RNN: A Dual-Phase Attention-Based Recurrent Neural Network Using Spatiotemporal LSTMs for Time Series Prediction 27th

DPAST-RNN: A Dual-Phase Attention-Based Recurrent Neural Net...

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27th International Conference on Neural Information Processing

作者： Shan, Shajia Shen, Ziyu Xi, Bin Liu, Zheng Li, Yun Nanjing Univ Posts & Telecommun Jiangsu Key Lab Fig Data Secur & Intelligent Proc Nanjing Peoples R China Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China

ISBN: (纸本)9783030638351;9783030638368

For time series forecasting, the weight distribution among multivariables and the long-short-term time dependence are always very important and challenging. Traditional machine forecasting can't automatically select the effective features of multivariable input and can't capture the time dependence of sequences. The key to solve this problem is to capture the spatial correlations at the same time, the spatiotemporal relationships at different times and the long-term dependence of the temporal relationships between different series. In this paper, inspired by human attention mechanism including encoder-decoder model, we propose DPAST-based RNN (DPAST-RNN) for long-term time series prediction. Specifically, in the first phase we use attention mechanism to extract relevant features at each time adaptively then we use stacked LSTM units to extract hidden information of time series both from time and space dimensions. In the second phase, we use another attention mechanism to select the related hidden state in encoder to the hidden state of the decoder at the current time to make context vector which is embed into recurrent neural network in decoder. Thorough empirical studies based upon the VM-Power dataset we collected on OpenStack and the NASDAQ 100 Stock dataset demonstrate that the DPAST-RNN can outperform state-of-the-art methods for time series prediction.

关键词： Time series prediction Spatiotemporal LSTM Attention mechanism encoder-decoder model

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