Bibliografías temáticas / Seq2seq

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Literatura académica sobre el tema "Seq2seq"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 26 de julio de 2025

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Artículos de revistas sobre el tema "Seq2seq"

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Aydın, Özlem, and Hüsein Kantarcı. "Türkçe Anahtar Sözcük Çıkarımında LSTM ve BERT Tabanlı Modellerin Karşılaştırılması." Bilgisayar Bilimleri ve Mühendisliği Dergisi 17, no. 1 (2024): 9–18. http://dx.doi.org/10.54525/bbmd.1454220.

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Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalış
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Sak, Semih, and Mustafa Alper Akkaş. "6G'de Nesnelerin İnterneti Teknolojisinin Medikal Alandaki Gelişmeleri." Bilgisayar Bilimleri ve Mühendisliği Dergisi 17, no. 1 (2024): 1–8. http://dx.doi.org/10.54525/bbmd.1454186.

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Günümüzde internet ortamında metne dayalı veri çok hızlı bir şekilde artış göstermektedir ve bu büyük veri içinden istenilen bilgiyi barındıran doğru içeriklere ulaşabilmek önemli bir ihtiyaçtır. İçeriklere ait anahtar sözcüklerin bilinmesi bu ihtiyacı karşılamada olumlu bir etki sağlayabilmektedir. Bu çalışmada, doğal dil işleme ve derin öğrenme modelleri ile Türkçe metinleri temsil eden anahtar sözcüklerin belirlenmesi amaçlanmıştır. Veri kümesi olarak Türkçe Etiketli Metin Derlemi ve Metin Özetleme-Anahtar Kelime Çıkarma Veri Kümesi birlikte kullanılmıştır. Derin öğrenme modeli olarak çalış
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Jin, Weihua, Shijie Zhang, Bo Sun, Pengli Jin, and Zhidong Li. "An Analytical Investigation of Anomaly Detection Methods Based on Sequence to Sequence Model in Satellite Power Subsystem." Sensors 22, no. 5 (2022): 1819. http://dx.doi.org/10.3390/s22051819.

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The satellite power subsystem is responsible for all power supply in a satellite, and is an important component of it. The system’s performance has a direct impact on the operations of other systems as well as the satellite’s lifespan. Sequence to sequence (seq2seq) learning has recently advanced, gaining even more power in evaluating complicated and large-scale data. The potential of the seq2seq model in detecting anomalies in the satellite power subsystem is investigated in this work. A seq2seq-based scheme is given, with a thorough comparison of different neural-network cell types and level
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Palasundram, Kulothunkan, Nurfadhlina Mohd Sharef, Khairul Azhar Kasmiran, and Azreen Azman. "SEQ2SEQ++: A Multitasking-Based Seq2seq Model to Generate Meaningful and Relevant Answers." IEEE Access 9 (2021): 164949–75. http://dx.doi.org/10.1109/access.2021.3133495.

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Bo, Tao, Weiyi Li, and Yue Liu. "A Technical Review of Sequence-to-Sequence Models." Academic Journal of Natural Science 2, no. 2 (2025): 1–9. https://doi.org/10.70393/616a6e73.323834.

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Seq2Seq models and their variants have become a mainstay of modern natural language processing and sequence modelling tasks. Just Information about Seq2Seq models. In this paper, we provide a comprehensive overview of the evolution of Seq2Seq architecture from early-stage RNN based approaches to recent Transformer based methods. The paper extensively covers additional important methods such as attention mechanisms, bidirectional encoders, pointer-generator networks, as well as optimization methods such as beam search, scheduled sampling and reinforcement learning. It also discusses the challen
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Palasundram, Kulothunkan, Nurfadhlina Mohd Sharef, Nurul Amelina Nasharuddin, Khairul Azhar Kasmiran, and Azreen Azman. "Sequence to Sequence Model Performance for Education Chatbot." International Journal of Emerging Technologies in Learning (iJET) 14, no. 24 (2019): 56. http://dx.doi.org/10.3991/ijet.v14i24.12187.

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Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intel-ligence based. However, unlike the ruled-based chatbots, artificial intelli-gence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of th
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Zhou, Lijian, Lijun Wang, Zhiang Zhao, Yuwei Liu, and Xiwu Liu. "A Seq2Seq Model Improved by Transcendental Learning and Imaged Sequence Samples for Porosity Prediction." Mathematics 11, no. 1 (2022): 39. http://dx.doi.org/10.3390/math11010039.

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Since the accurate prediction of porosity is one of the critical factors for estimating oil and gas reservoirs, a novel porosity prediction method based on Imaged Sequence Samples (ISS) and a Sequence to Sequence (Seq2Seq) model fused by Transcendental Learning (TL) is proposed using well-logging data. Firstly, to investigate the correlation between logging features and porosity, the original logging features are normalized and selected by computing their correlation with porosity to obtain the point samples. Secondly, to better represent the depositional relations with depths, an ISS set is e
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Geng, Xiaoran, Yue Ma, Wennian Cai, et al. "Evaluation of models for multi-step forecasting of hand, foot and mouth disease using multi-input multi-output: A case study of Chengdu, China." PLOS Neglected Tropical Diseases 17, no. 9 (2023): e0011587. http://dx.doi.org/10.1371/journal.pntd.0011587.

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Background Hand, foot and mouth disease (HFMD) is a public health concern that threatens the health of children. Accurately forecasting of HFMD cases multiple days ahead and early detection of peaks in the number of cases followed by timely response are essential for HFMD prevention and control. However, many studies mainly predict future one-day incidence, which reduces the flexibility of prevention and control. Methods We collected the daily number of HFMD cases among children aged 0–14 years in Chengdu from 2011 to 2017, as well as meteorological and air pollutant data for the same period.
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Nishtar, Zuhaib, and Jamil Afzal. "Seq2Seq-Based-Day-Ahead Scheduling for SCUC in Islanded Power Systems with Limited Intermittent Generation." Journal of Engineering, Science and Technological Trends 1, no. 1 (2024): 43–50. http://dx.doi.org/10.48112/jestt.v1i1.683.

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Due to their dependence on intermittent renewable energy sources, island power systems, which are generally located in remote places or on islands, offer particular issues for day-ahead scheduling. Using the capabilities of neural networks, we offer a Seq2Seq-based technique for day-ahead scheduling, which increases the precision and flexibility of unit commitment choices. The attention mechanisms in the Seq2Seq model are trained with historical data that includes projections for intermittent generation, demand, and unit commitment choices. The model is tested for its capacity to incorporate d
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Zhang, Gang. "A study of Grammar Analysis in English Teaching With Deep Learning Algorithm." International Journal of Emerging Technologies in Learning (iJET) 15, no. 18 (2020): 20. http://dx.doi.org/10.3991/ijet.v15i18.15425.

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In English teaching, grammar is a very important part. Based on the seq2seq model, a grammar analysis method combining the attention mechanism, word embedding and CNN seq2seq was designed using the deep learning algorithm, then the algorithm training was completed on NUCLE, and it was tested on CoNIL-2014. The experimental results showed that of seq2seq+attention improved 33.43% compared to the basic seq2seq; in the comparison between the method proposed in this study and CAMB, the P value of the former was 59.33% larger than that of CAMB, the R value was 8.9% larger, and the value of was 42.9
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Tesis sobre el tema "Seq2seq"

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Granstedt, Jason Louis. "Data Augmentation with Seq2Seq Models." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/78315.

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Paraphrase sparsity is an issue that complicates the training process of question answering systems: syntactically diverse but semantically equivalent sentences can have significant disparities in predicted output probabilities. We propose a method for generating an augmented paraphrase corpus for the visual question answering system to make it more robust to paraphrases. This corpus is generated by concatenating two sequence to sequence models. In order to generate diverse paraphrases, we sample the neural network using diverse beam search. We evaluate the results on the standard VQA validati
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Song, Shiping. "Study of Semi-supervised Deep Learning Methods on Human Activity Recognition Tasks." Thesis, KTH, Robotik, perception och lärande, RPL, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241366.

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This project focuses on semi-supervised human activity recognition (HAR) tasks, in which the inputs are partly labeled time series data acquired from sensors such as accelerometer data, and the outputs are predefined human activities. Most state-of-the-art existing work in HAR area is supervised now, which relies on fully labeled datasets. Since the cost to label the collective instances increases fast with the increasing scale of data, semi-supervised methods are now widely required. This report proposed two semi-supervised methods and then investigated how well they perform on a partly label
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Sandström, Emil. "Molecular Optimization Using Graph-to-Graph Translation." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172584.

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Drug development is a protracted and expensive process. One of the main challenges indrug discovery is to find molecules with desirable properties. Molecular optimization is thetask of optimizing precursor molecules by affording them with desirable properties. Recentadvancement in Artificial Intelligence, has led to deep learning models designed for molecularoptimization. These models, that generates new molecules with desirable properties, have thepotential to accelerate the drug discovery. In this thesis, I evaluate the current state-of-the-art graph-to-graph translation model formolecular o
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Lindén, Johannes. "Huvudtitel: Understand and Utilise Unformatted Text Documents by Natural Language Processing algorithms." Thesis, Mittuniversitetet, Avdelningen för informationssystem och -teknologi, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-31043.

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News companies have a need to automate and make the editors process of writing about hot and new events more effective. Current technologies involve robotic programs that fills in values in templates and website listeners that notifies the editors when changes are made so that the editor can read up on the source change at the actual website. Editors can provide news faster and better if directly provided with abstracts of the external sources. This study applies deep learning algorithms to automatically formulate abstracts and tag sources with appropriate tags based on the context. The study
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Raminella, Marco. "Predizione real-time da dati di sensori impiantistici e ambientali." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18643/.

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L'utilizzo dell'Intelligenza Artificiale in ambito industriale sta prendendo piede negli ultimi anni e il caso studiato in questa tesi ne è la prova. Lo sviluppo della tecnologia ha reso disponibile sempre più potenza computazionale a minor prezzo, rendendo possibile l'utilizzo delle Reti Neurali Profonde, studiate fin dagli anni ottanta, in un modo che fino a non molti anni fa era economicamente insostenibile. Si andrà a vedere il caso concreto della realizzazione di un sistema che esegue previsioni in tempo reale su telemetrie di un impianto per la gestione delle acque, con lo scopo di assis
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Holcner, Jonáš. "Strojový překlad pomocí umělých neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-386020.

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The goal of this thesis is to describe and build a system for neural machine translation. System is built with recurrent neural networks - encoder-decoder architecture in particular. The result is a nmt library used to conduct experiments with different model parameters. Results of the experiments are compared with system built with the statistical tool Moses.
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Che-Cheng, Yu, and 游哲誠. "A Seq2seq Generation-Based Approach to Short Text Conversation." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/j5n89x.

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碩士<br>朝陽科技大學<br>資訊工程系<br>106<br>Dialogue systems are quite common and used by many companies. But most of the dialogue system use rule-based or retrieve-based approach to reply users. The replies are predefined with less diversity. In recent years, many studies try to build systems that can generate responses. In this study, we use advanced learning technology Seq2seq to build such a system that can generate a short dialog (Short text conversation, STC). We participate the NTCIR-13 on STC-2 Generation-based sub-task. Organizers provided simplified Chinese data set. But most of the data is not
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Bo, Xian LI, and 李柏賢. "Apply seq2seq to machine learning for extend Chatbot content." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/7nwt74.

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Huai-HungHuang and 黃懷鋐. "Follow-up Question Generation using Pattern-based Seq2seq with a Small Corpus for Interview Coaching." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/nr3cf8.

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碩士<br>國立成功大學<br>資訊工程學系<br>106<br>Participation in an admission interview is a common, critical procedure before entering a school. However, students rarely seek professional help even if the best approach is to ask questions of an expert interviewer. To some degree, students lack interview experience and are unable to perform well in such important interviews. The reason for this may include both time and financial considerations. In light of this, in this thesis, an interview system is developed that repeatedly trains a student with questions that frequently appear in interview situations. In
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Libros sobre el tema "Seq2seq"

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Bansal, Ashish. Advanced Natural Language Processing with TensorFlow 2: Build effective real-world NLP applications using NER, RNNs, seq2seq models, Transformers, and more. Packt Publishing, 2021.

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Capítulos de libros sobre el tema "Seq2seq"

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Qi, Yu, and Jun Zhang. "Seq2seq-Based Generative Chatbot." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3161-2_22.

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Ji, Zhi. "A Multi-modal Seq2seq Chatbot Framework." In Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2456-9_24.

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AbstractThe pandemic has forced young people to stay away from school and friends, complete online learning at home and live at home. Therefore, various mental illnesses such as anxiety and depression occur more frequently. Chatbot is a communication method that is more acceptable to young people. This paper proposes a multi-modal chatbot seq2seq framework, which divides the mental state of young people into different types through multi-modal information such as text and images entered by users in the chatbot. This model combines image description and text summarization modules with the atten
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Bhutani, Nancy, Soumen Pachal, and Avinash Achar. "Seq2Seq RNNs for Bus Arrival Time Prediction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-96-0026-7_22.

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Lafkiar, Said, Alami Hamza, Mohamed Zouitni, Nabil Burmani, Hassan Badir, and Noureddine En Nahnahi. "Attentive Neural Seq2Seq for Arabic Question Generation." In International Conference on Advanced Intelligent Systems for Sustainable Development. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26384-2_71.

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Choi, Soohyeon, Rhongho Jang, DaeHun Nyang, and David Mohaisen. "Untargeted Code Authorship Evasion with Seq2Seq Transformation." In Computational Data and Social Networks. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0669-3_8.

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Xu, Yige, Yichao Luo, Yicheng Zhou, et al. "Searching Effective Transformer for Seq2Seq Keyphrase Generation." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88483-3_7.

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Nanayakkara, Gayani, Nirmalie Wiratunga, David Corsar, Kyle Martin, and Anjana Wijekoon. "Clinical Dialogue Transcription Error Correction Using Seq2Seq Models." In Multimodal AI in Healthcare. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14771-5_4.

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Dash, Aniket, Aman Singh, Akshat Jain, et al. "Stock Price Analysis and Prediction Using Seq2Seq LSTM." In Proceedings of International Conference on Data Analytics and Insights, ICDAI 2023. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3878-0_56.

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Chen, Xiaolong, Jieren Cheng, Zhixin Rong, Wenghang Xu, Shuai Hua, and Zhu Tang. "Multi-label Text Classification Based on Improved Seq2Seq." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9243-0_43.

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Alghamdi, Jawaher, and Zi Huang. "Modeling Daily Crime Events Prediction Using Seq2Seq Architecture." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69377-0_16.

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Actas de conferencias sobre el tema "Seq2seq"

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Liu, Pan, Xiaohua Tian, and Zhouhan Lin. "Enable Fast Sampling for Seq2Seq Text Diffusion." In Findings of the Association for Computational Linguistics: EMNLP 2024. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.findings-emnlp.497.

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V, Rahul Chiranjeevi, Senthil Pandi S, Keerthana H, Abhignya P, and Rajendiran M. "Chatbot for Government Schemes Using SEQ2SEQ Model." In 2024 Second International Conference on Advances in Information Technology (ICAIT). IEEE, 2024. http://dx.doi.org/10.1109/icait61638.2024.10690438.

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Singhal, Bhavuk, Anshu Aditya, Lokesh Todwal, Shubham Jain, and Debashis Mukherjee. "GeoIndia: A Seq2Seq Geocoding Approach for Indian Addresses." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.29.

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Xu, Zheng, Sheng Wang, Feiyun Zhu, and Junzhou Huang. "Seq2seq Fingerprint." In BCB '17: 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. ACM, 2017. http://dx.doi.org/10.1145/3107411.3107424.

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Setiawan, Bambang Abdi, Ema Utami, and Anggit Dwi Hartanto. "Banjarese Chatbot Using Seq2Seq Model." In 2021 4th International Conference on Information and Communications Technology (ICOIACT). IEEE, 2021. http://dx.doi.org/10.1109/icoiact53268.2021.9563915.

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Zhang, Hainan, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. "Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/635.

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Sequence to sequence (Seq2Seq) approach has gained great attention in the field of single-turn dialogue generation. However, one serious problem is that most existing Seq2Seq based models tend to generate common responses lacking specific meanings. Our analysis show that the underlying reason is that Seq2Seq is equivalent to optimizing Kullback–Leibler (KL) divergence, thus does not penalize the case whose generated probability is high while the true probability is low. However, the true probability is unknown, which poses challenges for tackling this problem. Inspired by the fact that the coh
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Ge, DongLai, Junhui Li, Muhua Zhu, and Shoushan Li. "Modeling Source Syntax and Semantics for Neural AMR Parsing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/691.

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Sequence-to-sequence (seq2seq) approaches formalize Abstract Meaning Representation (AMR) parsing as a translation task from a source sentence to a target AMR graph. However, previous studies generally model a source sentence as a word sequence but ignore the inherent syntactic and semantic information in the sentence. In this paper, we propose two effective approaches to explicitly modeling source syntax and semantics into neural seq2seq AMR parsing. The first approach linearizes source syntactic and semantic structure into a mixed sequence of words, syntactic labels, and semantic labels, whi
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"Keyphrase Generation with a Seq2seq Model." In 2019 the 9th International Workshop on Computer Science and Engineering. WCSE, 2019. http://dx.doi.org/10.18178/wcse.2019.06.107.

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Liu, Bingran. "Neural Question Generation based on Seq2Seq." In ICMAI 2020: 2020 5th International Conference on Mathematics and Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3395260.3395275.

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Xu, Jian, Wei Luo, and Ying Huang. "Dadu River Runoff Forecasting via Seq2Seq." In AICS 2019: 2019 International Conference on Artificial Intelligence and Computer Science. ACM, 2019. http://dx.doi.org/10.1145/3349341.3349457.

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