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Computer Science 분야 (02)
Speech processing; Models; Dialogue policy
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Computer Science 분야 (02): Speech processing; Models; Dialogue policy
Speech processing; Models; Dialogue policy
Algorithms; Artificial intelligence; Bandit algorithm
Optimization; Learning systems; Hyperparameter optimization
Learning systems; Learning algorithms; Learning MTL
Learning systems; Artificial intelligence; Adversarial samples
Matrix algebra; Algorithms; Random projections
Fuzzy rules; Cognitive systems; Maps FCMs
Robots; Artificial intelligence; Lethal autonomous
Planning; Artificial intelligence; AI planning
Dynamic programming; Reinforcement learning; ADP algorithm
Research portfolio analysis and topic prominence
Klavans, R. & Boyack, K. W. (2017). Journal of Informetrics, 11,1158-1174.
피인용 상위 논문
Deep reinforcement learning for dialogue generation
Li, J., Monroe, W., Ritter, A. and 3 more (2016) EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings, pp. 1192-1202.
Recent neural models of dialogue generation offer great promise for generating responses for conversational agents, but tend to be shortsighted, predicting utterances one at a time while ignoring their influence on future outcomes. Modeling the future direction of a dialogue is crucial to generating coherent, interesting dialogues, a need which led traditional NLP models of dialogue to draw on reinforcement learning. In this paper, we show how to integrate these goals, applying deep reinforcement learning to model future reward in chatbot dialogue. The model simulates dialogues between two virtual agents, using policy gradient methods to reward sequences that display three useful conversational properties: informativity, coherence, and ease of answering (related to forward-looking function). We evaluate our model on diversity, length as well as with human judges, showing that the proposed algorithm generates more interactive responses and manages to foster a more sustained conversation in dialogue simulation. This work marks a first step towards learning a neural conversational model based on the long-term success of dialogues. © 2016 Association for Computational Linguistics
The second dialog state tracking challenge
Henderson, M., Thomson, B., Williams, J.
(2014) SIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference, pp. 263-272.
A spoken dialog system, while communicating with a user, must keep track of what the user wants from the system at each step. This process, termed dialog state tracking, is essential for a successful dialog system as it directly informs the system's actions. The first Dialog State Tracking Challenge allowed for evaluation of different dialog state tracking techniques, providing common testbeds and evaluation suites. This paper presents a second challenge, which continues this tradition and introduces some additional features-a new domain, changing user goals and a richer dialog state. The challenge received 31 entries from 9 research groups. The results suggest that while large improvements on a competitive baseline are possible, trackers are still prone to degradation in mismatched conditions. An investigation into ensemble learning demonstrates the most accurate tracking can be achieved by combining multiple trackers. © 2014 Association for Computational Linguistics.
The conversational interface: Talking to smart devices.
McTear, M., Callejas, Z., Griol, D.
(2016) The Conversational Interface: Talking to Smart Devices, pp. 1-422.
Presents a comprehensive overview of the various technologies that underlie conversational user interfaces Combines descriptions of the technologies with a guide to various toolkits and software that enable readers to implement and test their own solutions Provides a series of worked examples so readers can develop and implement different aspects of the technologies. © Springer International Publishing 15 Switzerland 2016.
Word-based dialog state tracking with recurrent neural networks
Henderson, M., Thomson, B., Young, S.
(2014) SIGDIAL 2014 - 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Proceedings of the Conference, pp. 292-299.
Recently discriminative methods for tracking the state of a spoken dialog have been shown to outperform traditional generative models. This paper presents a new wordbased tracking method which maps directly from the speech recognition results to the dialog state without using an explicit semantic decoder. The method is based on a recurrent neural network structure which is capable of generalising to unseen dialog state hypotheses, and which requires very little feature engineering. The method is evaluated on the second Dialog State Tracking Challenge (DSTC2) corpus and the results demonstrate consistently high performance across all of the metrics. © 2014 Association for Computational Linguistics.
A network-based end-to-end trainable task-oriented dialogue system.
Wen, T.-H., Vandyke, D., Mrkšíc, N. and 5 more (2017) 15th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2017 - Proceedings of Conference, 1, pp. 438-449.
Teaching machines to accomplish tasks by conversing naturally with humans is challenging. Currently, developing taskoriented dialogue systems requires creating multiple components and typically this involves either a large amount of handcrafting, or acquiring costly labelled datasets to solve a statistical learning problem for each component. In this work we introduce a neural network-based text-in, textout end-to-end trainable goal-oriented dialogue system along with a new way of collecting dialogue data based on a novel pipe-lined Wizard-of-Oz framework. This approach allows us to develop dialogue systems easily and without making too many assumptions about the task at hand. The results show that the model can converse with human subjects naturally whilst helping them to accomplish tasks in a restaurant search domain. © 2017 Association for Computational Linguistics.
주요 투고 저널
연도별 연구 성과 편수 (논문 + 학술대회)
연도별 총 피인용수
주요 연구 국가
주요 연구 기관
Algorithms; Artificial intelligence; Bandit algorithm >>
Jul 15, 2020 11:23 AM
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