A Survey on Dialogue Systems: Recent Advances and New Frontiers

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545

Distributed Representations of Words and Phrases and their Compositionality

Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213

Advances in Neural Information Processing Systems 25

Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653

Term-Weighting Approaches in Automatic Text Retrieval

Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 11,421

Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning

Williams, R. J. (1992) · Machine Learning · doi:10.1007/bf00992696 · Cited by 8,608

Recurrent neural network based language model

Mikolov, T., Karafiát, M., Burget, L., Černocký, J., & Khudanpur, S. (2010) · Interspeech 2010 · doi:10.21437/interspeech.2010-343 · Cited by 5,390

Artificial Intelligence in Enhancing Korean High School Students’ English Skills

Kim, R. (2025) · New language learning and teaching environments · doi:10.1007/978-3-031-83561-2_5 · Cited by 5

Measuring User Experience Through Speech Analysis: Insights from HCI Interviews

Ma, Y., Zhang, X., Zhang, Y., & Fjeld, M. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719734 · Cited by 4

Agentic, Multimodal Large Language Model (LLM) with behavioural parameters and self-iterative capabilities for conversational architectural design processes

Cheung, L. H., Lombardi, D., & Marco, G. D. (2025) · Architectural Science Review · doi:10.1080/00038628.2025.2586655 · Cited by 6

A Critical Analysis of EmpatheticDialogues as a Corpus for Empathetic Engagement

Debnath, A., & Conlan, O. (2023) · doi:10.1145/3588967.3588973 · Cited by 4

Question Rewrite Based Dialogue Response Generation

Liu, H., Rong, W., Shi, L., Ouyang, Y., & Xiong, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04224-0_15 · Cited by 4

A Multi Domain Knowledge Enhanced Matching Network for Response Selection in Retrieval-Based Dialogue Systems

Chen, X., Chen, F., Xu, S., & Xu, B. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9746656 · Cited by 4

PHAED: A Speaker-Aware Parallel Hierarchical Attentive Encoder-Decoder Model for Multi-Turn Dialogue Generation

Wang, Z., Jiang, M., & Wang, J. (2023) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2023.3316472 · Cited by 7

Multilingual mixture attention interaction framework with adversarial training for cross-lingual SLU

Zhang, Q., Wang, S., & Li, J. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09132-5 · Cited by 4

Debiasing Counterfactual Context With Causal Inference for Multi-Turn Dialogue Reasoning

Wang, X., Zhang, H., Zhao, S., Chen, H., Ding, Z., Wan, Z., Cheng, B. H., & Lan, Y. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3343608 · Cited by 5

Natural language processing-based approach for automatically coding ship sensor data

Kim, Y., Park, K.-P., & Yoo, B.-W. (2023) · International Journal of Naval Architecture and Ocean Engineering · doi:10.1016/j.ijnaoe.2023.100581 · Cited by 4

Label Hierarchical Structure-Aware Multi-Label Few-Shot Intent Detection via Prompt Tuning

Zhang, X., Li, X., Liu, H., Liu, X., & Zhang, X. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657947 · Cited by 4

Dialogue agents 101: a beginner’s guide to critical ingredients for designing effective conversational systems

Kumar, S., Bhatia, S., Aggarwal, M., & Chakraborty, T. (2024) · Natural language processing. · doi:10.1017/nlp.2024.42 · Cited by 5

Optimizing pipeline task-oriented dialogue systems using post-processing networks

Ohashi, A., & Higashinaka, R. (2024) · Computer Speech & Language · doi:10.1016/j.csl.2024.101742 · Cited by 4

Boosting Discriminability for Robust Multimodal Entity Linking with Visual Modality Missing

Lao, M., Li, Z., Guo, Y., Zhang, X., Cai, S., Ding, Z., & Li, H. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729906 · Cited by 4

Overview of the NLPCC 2020 Shared Task: AutoIE

Yang, X., Wu, B., Jie, Z., & Liu, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60457-8_46 · Cited by 4

Persian Language Understanding in Task-Oriented Dialogue System for Online Shopping

Borhanifard, Z., Basafa, H., Razavi, S. Z., & Faili, H. (2020) · International Conference on Information and Knowledge Technology (IKT) · doi:10.1109/ikt51791.2020.9345639 · Cited by 4

Keep and Select: Improving Hierarchical Context Modeling for Multi-Turn Response Generation

Ling, Y., Cai, F., Liu, J., Chen, H., & de Rijke, M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3112700 · Cited by 4

Recent Developments in Chatbot Usability and Design Methodologies

Kuhail, M. A., Farooq, S., & Almutairi, S. (2023) · Advances in web technologies and engineering book series · doi:10.4018/978-1-6684-6234-8.ch001 · Cited by 4

RSP-DST: Revisable State Prediction for Dialogue State Tracking

Li, Q., Zhang, W., Huang, M., Feng, S., & Wu, Y. (2023) · Electronics · doi:10.3390/electronics12061494 · Cited by 4

A Recommendation-type Dialogue System Responding to Potential Requests in Consideration of Personal Attributes

Seki, Y., & Ueno, Y. (2023) · Information and Technology in Education and Learning · doi:10.12937/itel.3.1.trans.p003 · Cited by 4

Mutually improved response generation and dialogue summarization for multi-domain task-oriented dialogue systems

Zhao, M., Wang, L., Ji, H., Jiang, Z., Li, R., Lu, X., & Hu, Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110927 · Cited by 4

Memories for Virtual AI Characters

Landwehr, F., Doggett, E. V., & Weber, R. M. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-main.17 · Cited by 4

Opinion attribution improves motivation to exchange subjective opinions with humanoid robots

Uchida, T., Minato, T., & Ishiguro, H. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1175879 · Cited by 4

Leveraging BERT for Natural Language Understanding of Domain-Specific Knowledge

Iga, V. I. R., & Silaghi, G. C. (2023) · International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) · doi:10.1109/synasc61333.2023.00035 · Cited by 4

The Sogou Spoken Language Understanding System for the NLPCC 2018 Evaluation

Gong, N., Shen, T., Wang, T., Qi, D., Li, M., Wang, J., & Li, C.-H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-99495-6_38 · Cited by 4

Comparative Study of Topology and Feature Variants for Non-Task-Oriented Chatbot using Sequence to Sequence Learning

Dzakwan, G., & Purwarianti, A. (2018) · International Conference on Advanced Informatics: Concepts, Theory and Applications (ICAICTA) · doi:10.1109/icaicta.2018.8541285 · Cited by 4

Knowledge-Aware Self-Attention Networks for Document Grounded Dialogue Generation

Tang, X., & Hu, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29563-9_35 · Cited by 4

Dialogue Management with Deep Reinforcement Learning: Balancing Exploration and Exploitation

Nishimoto, B. E., & Costa, A. H. R. (2019) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2019.00085 · Cited by 4

Predictable and Adaptive Goal-oriented Dialog Policy Generation

Le, N. X. T., Siddique, A. B., Jamour, F., Oymak, S., & Hristidis, V. (2021) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc50631.2021.00012 · Cited by 6

Ontology Population Reusing Resources for Dialogue Intent Detection: Generic and Multilingual Approach

Aceta, C., Fernández, I., & Soroa, A. (2021) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-072-4_002 · Cited by 4

Data generation approaches for topic classification in multilingual spoken dialog systems

Montenegro, C., Santana, R., & Lozano, J. A. (2019) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/3316782.3316792 · Cited by 4

Goal-Oriented Modelling for Virtual Assistants

Leung, J. C., Shen, Z., & Miao, C. (2019) · IEEE International Conference on Agents (ICA) · doi:10.1109/agents.2019.8929177 · Cited by 4

Negation and Speculation Detection. By Noa P. Cruz Diáz and Manual J. Maña López (University of Huelva). John Benjamins (Natural Language Processing, 13), ix+95 pp; hardcover, ISBN 9789028202178, USD 143.00, EUR 95.00; paperback ISBN 9789027202161, USD 49.95; EUR 33.00; e-book, ISBN 9789027262950, https://doi.org/10.1075nlp.13

Gîrju, R. (2019) · Computational Linguistics · doi:10.1162/coli_r_00365 · Cited by 4

Bayesian Deep Multi-Agent Multimodal Reinforcement Learning for Embedded Systems in Games, Natural Language Processing and RoboticsPreprint

Kourouklides, I. (2022) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/sjrkh · Cited by 4

OSTOD: One-Step Task-Oriented Dialogue with activated state and retelling response

Huang, H., Yang, P., Wei, W., Shi, S., & Mao, X.-L. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111677 · Cited by 3

Unveiling Information Through Narrative In Conversational Information Seeking

Javadi, V. S., Trippas, J. R., & Flek, L. (2024) · ACM Conference on Conversational User Interfaces (CUI) · doi:10.1145/3640794.3665884 · Cited by 3

Towards leveraging conversational agents for instructors and learners to find and access learning resources

Steinbeck, H., Zobel, T. E. I., & Meinel, C. (2021) · World Engineering Education Forum - Global Engineering Deans Council (WEEF-GEDC) · doi:10.1109/weef/gedc53299.2021.9657307 · Cited by 3

Robo-Advisors: A Big Data Challenge

Severino, F., & Thierry, S. (2022) · Springer eBooks · doi:10.1007/978-3-031-12240-8_7 · Cited by 3

Conversation-Driven Refinement of Knowledge Graphs: True Active Learning with Humans in the Chatbot Application Loop

Buhl, D., Szafarski, D., Welz, L., & Lanquillon, C. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35894-4_3 · Cited by 3

Study and Comparative Analysis of ChatGPT, GPT and DAll-E2

Jhajaria, S., & Kaur, D. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10307823 · Cited by 4

Evolving trends, limitations, and ethical considerations in AI-driven conversational interfaces: assessing ChatGPT's impact on healthcare, financial services, and educational sectors

Afjal, M. (2024) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2024.2420617 · Cited by 4

Empathic Dialog Systems for Patient Intake: Balancing Task-Completion and Emotional Support using RAGs

Shayaninasab, M., Zahoor, M., & Yalçın, Ö. N. (2024) · ACM International Conference on Intelligent Virtual Agents (IVA) · doi:10.1145/3652988.3673968 · Cited by 3

Predict-Then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems

Lin, Z., Cui, S., Li, G., Kang, X., Ji, F. Q., Li, F.-L., Zhao, Z., Chen, H., & Zhang, Y. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3110145 · Cited by 3

Relation-aware Bidirectional Path Reasoning for Commonsense Question Answering

Wang, J., Li, X., Tan, Z., Zhao, X. H., & Xiao, W. (2021) · Conference on Computational Natural Language Learning · doi:10.18653/v1/2021.conll-1.35 · Cited by 3

Considerations on creating conversational agents for multiple environments and users

Cebrián, J., Martínez, R., Rodríguez, N., & D’Haro, L. F. (2021) · AI Magazine · doi:10.1609/aimag.v42i2.7484 · Cited by 4

Flexible entity marks and a fine-grained style control for knowledge based natural answer generation

Huang, Y., Yang, M., & Yang, N. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108248 · Cited by 3

A Review on Approaches in Arabic Chatbot for Open and Closed Domain Dialog

Alsubayhay, A. M. S., Salam, M. S. H., & Mohamed, F. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131117 · Cited by 3

Building Conventional “Experts” With a Dialogue Logic Programming Language

Zheng, X. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3271119 · Cited by 3

Development and application of a communication robot to improve the emotional state of elderly living alone

Tsujikawa, S., Tsutsui, H., & Honda, Y. (2023) · Universal Access in the Information Society · doi:10.1007/s10209-023-01007-9 · Cited by 3

Assistant Solutions in Software Engineering: A Systematic Literature Review

Carpio, Á. F. D., & Angarita, L. B. (2023) · IEEE International Conference on Software Engineering and Service Science (ICSESS) · doi:10.1109/icsess58500.2023.10293029 · Cited by 3

AIDA-Bot 2.0: Enhancing Conversational Agents with Knowledge Graphs for Analysing the Research Landscape

Meloni, A., Angioni, S., Salatino, A. A., Osborne, F., Birukou, A., Recupero, D. R., & Motta, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47243-5_22 · Cited by 3

A multi-perspective global–local interaction framework for identifying dialogue acts and sentiments of dialogue utterances jointly

Zhang, Q., & Li, J. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02010-5 · Cited by 3

FacetCRS: Multi-Faceted Preference Learning for Pricking Filter Bubbles in Conversational Recommender System

Zheng, Y., Chen, Z., Qin, J., & Lin, L. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i8.28794 · Cited by 3

Span Attention for Entity-Consistent Task-Oriented Dialogue Response Generation

Chen, J., Dong, X., Xie, W., Gong, T., Wang, F. L., & Hao, T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10887791 · Cited by 3

Intent Recognition in Dialogue Systems

Ran, Z., Chen, Y., Jiang, Q., & Kunpeng, E. (2024) · Asia-Pacific Artificial Intelligence and Big Data Forum (AIBDF) · doi:10.1145/3718491.3718520 · Cited by 4

Integrating AI Planning with Natural Language Processing: A Combination of Explicit and Tacit Knowledge

Jin, K.-B., & Zhuo, H. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3729236 · Cited by 14

Agent-Initiated Interaction in Phone UI Automation

Kahlon, N., Rom, G., Efros, A., Galgani, F., Berkovitch, O., Caduri, S., Bishop, W. E., Riva, O., & Dagan, I. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717526 · Cited by 3

A classifier expansion framework with dual knowledge distillation and dynamic weighting for continual relation extraction

Mao, A., Wu, D., Jiang, L., Song, S., Li, Y., Huang, H., & Slamu, W. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07488-0 · Cited by 3

MOOC Guider: An End-to-End Dialogue System for MOOC Users

Li, Y., & Zhang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01298-4_23 · Cited by 3

Show, Price and Negotiate: A Negotiator With Online Value Look-Ahead

Parvaneh, A., Abbasnejad, E., Wu, Q., Shi, J. Q., & van den Hengel, A. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3065169 · Cited by 8

Sentence model based subword embeddings for a dialog system

Chung, E., Kim, H. W., & Song, H. J. (2022) · ETRI Journal · doi:10.4218/etrij.2020-0245 · Cited by 3

A Heterogeneous Interaction Graph Network for Multi-Intent Spoken Language Understanding

Zhang, Q., Wang, S., & Li, J. (2023) · Neural Processing Letters · doi:10.1007/s11063-023-11210-7 · Cited by 3

Non-goal oriented dialogue agents: state of the art, dataset, and evaluation

Mehndiratta, A., & Asawa, K. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09848-z · Cited by 4

Goal-oriented framework for AI-empowered curriculum design

Wang, H. (2024) · doi:10.32657/10356/181505 · Cited by 3

IvCDS: An End-to-End Driver Simulator for Personal In-Vehicle Conversational Assistant

Ji, T., Yin, X., Cheng, P., Zhou, L., Liu, S., Bao, W., & Lyu, C. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192315493 · Cited by 2

IoT-AID: An Automated Decision Support Framework for IoT

Choaib, M., Garouani, M., Bouneffa, M., Waldhoff, N., & Mohanna, Y. (2024) · SN Computer Science · doi:10.1007/s42979-024-02780-x · Cited by 2

Task-based dialogue policy learning based on diffusion models

Liu, Z., Pang, R., & Dong, Z. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05810-6 · Cited by 2

Major NLP Applications

Lee, R. (2025) · Natural Language Processing · doi:10.1007/978-981-96-3208-4_9 · Cited by 2

Between Control and Collaboration: Artistic Autonomy in AI-Generated Visual Artworks

Puadi, M. F., Hashim, M. E. A., & Albakry, N. S. (2025) · Semarak International Journal of Creative Art and Design · doi:10.37934/sijcad.4.1.111 · Cited by 2

Endlessly Patient, Never Judgmental, or Flexibly Opinionated: Imagining a Future of Meaningful Conversations With Technology

Neuhaus, R., Laschke, M., Ringfort-Felner, R., & Hassenzahl, M. (2025) · International Academic Mindtrek Conference (Mindtrek) · doi:10.1145/3757980.3758001 · Cited by 2

A Deep Semantic Matching Network for Answer Selection

Li, Y., Li, H., Li, J., Zhang, N., & Yuan, G. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866166 · Cited by 2

Evaluation of Synthetic Datasets Generation for Intent Classification Tasks in Portuguese

Paula, R. T., Neto, D. G. D. A., Romero, D., & Guerra, P. T. (2021) · Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL) · doi:10.5753/stil.2021.17806 · Cited by 2

Evaluating Neural Dialogue Systems Using Deep Learning and燙onversation燞istory

AlMutairi, I. A., & Qamar, A. M. (2022) · Journal on artificial intelligence · doi:10.32604/jai.2022.032390 · Cited by 2

Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

Sakai, Y., & Matsumoto, M. (2023) · IEEJ Journal of Industry Applications · doi:10.1541/ieejjia.22006784 · Cited by 2

DIAGRAPH: An Open-Source Graphic Interface for Dialog Flow Design

Väth, D., Vanderlyn, L., & Vu, N. T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-demo.13 · Cited by 2

Towards Zero-Shot Multilingual Transfer for Code-Switched Responses

Wu, T.-W., Zhao, C., Chang, E., Shi, Y., Chuang, P., Chandra, V., & Juang, B. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.417 · Cited by 2

Improving Faithfulness and Factuality with Contrastive Learning in Explainable Recommendation

Zhuang, H., Zhang, W. E., Chen, W., Yang, J. Y., & Sheng, Q. Z. (2024) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3653984 · Cited by 5

RepEKShot: an evidential k-nearest neighbor classifier with repulsion loss for few-shot named entity recognition

Liu, H., Peng, W., & Song, J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06244-0 · Cited by 2

DialTest‐EA: An Enhanced Fuzzing Approach With Energy Adjustment for Dialogue Systems via Metamorphic Testing

Chen, H., Chen, J., Wu, Y., Cai, S., Ahmad, B., Huang, R., Wang, S., & Zhang, C. (2024) · Software Testing Verification and Reliability · doi:10.1002/stvr.1897 · Cited by 2

Aircraft human‐machine interaction assistant design: A novel multimodal data processing and application framework

Wu, A., Yang, J., Lv, M., Li, H., Li, L., & Yang, R. (2024) · IET Control Theory and Applications · doi:10.1049/cth2.12754 · Cited by 2

Adversarial botometer: adversarial analysis for social bot detection

Najari, S., Rafiei, D., Salehi, M., & Farahbakhsh, R. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01387-7 · Cited by 2

PRIMAL: Prompting Multiple Language Models for Low-Resource Diverse Response Generation

Wen, Z., Tian, Z., Pan, S., Zhu, K., Meng, X., Song, Y., & Li, D. (2024) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3507571 · Cited by 2

Reinforcement Learning in Generative AI: State-of-the-Art Performance

Kanthavel, R., Freeda, R. A., & Dhaya, R. (2025) · Information systems engineering and management · doi:10.1007/978-3-031-91660-1_4 · Cited by 2

ABC-FHE: A Resource-Efficient Accelerator Enabling Bootstrappable Parameters for Client-Side Fully Homomorphic Encryption

Yune, S., Lee, H., Putra, A., Cho, H., Manh, C. D., Jeon, J., & Kim, J. (2025) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac63849.2025.11132592 · Cited by 2

Towards Generating Stylistic Dialogues for Narratives Using Data-Driven Approaches

Xu, W., Hargood, C., Tang, W. J., & Charles, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-04028-4_53 · Cited by 3

Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems

Rabinovich, E., Vetzler, M., Boaz, D., Kumar, V., Pandey, G. S., & Tavor, A. A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-industry.22 · Cited by 5

A task-oriented dialogue bot using long short-term memory with attention for Thai language

Robloke, R., & Kijsirikul, B. (2019) · International Conference on Advanced Information Science and System (AISS) · doi:10.1145/3373477.3373704 · Cited by 2

FPSeq: Simplifying and Accelerating Task-Oriented Dialogue Systems via Fully Parallel Sequence-to-Sequence Framework

Song, M., Chen, Z., Niu, P., & Haihong, E. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00165 · Cited by 2

CDS: Contextual Spoken Language Understanding Model Based on Dual Semantics

Ma, K., Hu, M., Li, F., & Luo, L. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1651/1/012051 · Cited by 2

CAT-BERT: A Context-Aware Transferable BERT Model for Multi-turn Machine Reading Comprehension

Chen, C., Huang, X., Ji, F., Wang, C., Qiu, M., Huang, J., & Zhang, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73197-7_10 · Cited by 2

A Practical 2-step Approach to Assist Enterprise Question-Answering Live Chat

Liao, L.-Y., & Fares, T. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.48 · Cited by 2

'Could You Describe the Reason for the Transfer?'

Wang, Z., Wang, F., Zhang, H., Yang, M., Cao, S., Wen, Z., & Zhang, Z. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3481906 · Cited by 2

Densely Enhanced Semantic Network for Conversation System in Social Media

Li, Y., Wang, Z., & Yu, J. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3501799 · Cited by 2

Robo-Advisory

Xing, F., Cambria, E., & Welsch, R. E. (2019) · Socio-affective computing · doi:10.1007/978-3-030-30263-4_7 · Cited by 2

An Interactive Virtual Home Navigation System Based on Home Ontology and Commonsense Reasoning

Schalkwijk, A., Yatsu, M., & Morita, T. (2022) · Information · doi:10.3390/info13060287 · Cited by 2

A Survey on Intelligent Question and Answer Systems

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