A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Generating Persona-Consistent Dialogue Responses Using Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
Preserved Fulltext
We propose a novel approach to train transformer-based dialogue agents using actor-critic reinforcement learning. ...
We define a new reward function to assess generated responses in terms of persona consistency, topic consistency, and fluency. ...
We propose a novel approach to train dialogue agents with deep reinforcement learning (DeepRL). ...
arXiv:2005.00036v1
fatcat:downot5rifaxvnw23hmnv35fbe
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
the persona-consistency of generated responses. ...
Different from existing work that re-ranks the retrieved responses through an NLI model, we cast the task as a reinforcement learning problem and propose to exploit the NLI signals from response-persona ...
Reinforcement Learning We formalize the persona consistent dialogue generation problem as a reinforcement learning task. ...
arXiv:1911.05889v3
fatcat:rl3ta7j7wnchtgvbvpt4bej3ju
Generating Persona Consistent Dialogues by Exploiting Natural Language Inference
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
the persona-consistency of generated responses. ...
Different from existing work that re-ranks the retrieved responses through an NLI model, we cast the task as a reinforcement learning problem and propose to exploit the NLI signals from response-persona ...
Reinforcement Learning We formalize the persona consistent dialogue generation problem as a reinforcement learning task. ...
doi:10.1609/aaai.v34i05.6417
fatcat:46hhzqra5bavth7ct6lpz3g4gm
Deep Active Learning for Dialogue Generation
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational ...
We propose an online, end-to-end, neural generative conversational model for open-domain dialogue. ...
Researchers are now exploring Deep Reinforcement Learning (DRL) to address the hard problems of NLU and NLG in dialogue generation. ...
arXiv:1612.03929v5
fatcat:b26h4odcvnb6bffoozvxgm2evq
Deep Active Learning for Dialogue Generation
2017
Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (*SEM 2017)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Experiments show that our model inherently promotes the generation of semantically relevant and interesting responses, and can be used to train agents with customized personas, moods and conversational ...
We propose an online, end-to-end, neural generative conversational model for opendomain dialogue. ...
Researchers are now exploring Deep Reinforcement Learning (DRL) to address the hard problems of NLU and NLG in dialogue generation. ...
doi:10.18653/v1/s17-1008
dblp:conf/starsem/AsgharPJL17
fatcat:pfimq5yzf5bjnibxnxscqpkdsm
Recent Progress in Conversational AI
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
We will provide a brief review of the recent progress in the Conversational AI, including the commonly adopted techniques, notable works, famous competitions from academia and industry and widely used ...
In [49] , the author proposes a hierarchical deep reinforcement learning framework to solve the composite task-completion dialogue policy learning problem. ...
In [53] [40] , the author proposed an acceleration technique by using the Thompson sampling to improve the efficiency of the deep reinforcement learning(DQN). ...
arXiv:2204.09719v1
fatcat:lf7g4enbsfc2rona3ki3cuwsua
Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Other Versions
In this survey, we mainly focus on the deep learning based dialogue systems. ...
To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. ...
RL for knowledge grounded dialogue systems Some systems use reinforcement learning to select from outside information like persona, document, knowledge graph, etc., and generate responses accordingly. ...
arXiv:2105.04387v5
fatcat:yd3gqg45rjgzxbiwfdlcvf3pye
The Rapidly Changing Landscape of Conversational Agents
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
We look at statistical, neural, generative adversarial network based and reinforcement learning based approaches and how they evolved. ...
persona etc. ...
Actor-Critic Algorithm Deep reinforcement learning (RL) methods have significant potential for dialogue policy optimisation. ...
arXiv:1803.08419v2
fatcat:5huy2e6tmbexlfoampcdy6zhw4
Neural Approaches to Conversational AI
2018
Proceedings of ACL 2018, Tutorial Abstracts
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We group conversational systems into three categories: (1) question answering agents, (2) taskoriented dialogue agents, and (3) social bots. ...
a review of state-of-the-art neural approaches, draw the connection between neural approaches and traditional symbolic approaches, and discuss the progress we have made and challenges we are facing, using ...
., 2016b) presented a persona-based model to address the issue of speaker consistency in neural response generation. ...
doi:10.18653/v1/p18-5002
dblp:conf/acl/GaoGL18
fatcat:7llxwuntafh4fcjj4ia3tm642a
A Task-oriented Chatbot Based on LSTM and Reinforcement Learning
2022
ACM Transactions on Asian and Low-Resource Language Information Processing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Thanks to the advancements in deep learning, chatbots are widely used in messaging applications. Undoubtedly, a chatbot is a new way of interaction between humans and machines. ...
Hence, training a chatbot model that uses low-resource conversational data to generate more diverse dialogues is desirable. ...
The deep learning system with reinforcement learning generates a response given to an input of a user. The detailed description of the related topics is listed as follows. ...
doi:10.1145/3529649
fatcat:swzdgoy6ubahbmdd6mxim4c7mu
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
2019
2019 International Joint Conference on Neural Networks (IJCNN)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text-without any manual annotations. Experimental results using different splits of training data report the following. ...
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. ...
We present a novel approach based on the reinforcement learning [2] , unsupervised learning [3] and deep learning [4] paradigms. ...
doi:10.1109/ijcnn.2019.8852376
dblp:conf/ijcnn/CuayahuitlLRCHK19
fatcat:dvtylbfegrc2fey55hcguc3hti
State of the Art of User Simulation approaches for conversational information retrieval
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Reinforcement learning has emerged as a paradigm particularly suited to optimize sequential decision making in many domains and has recently appeared in IR. ...
However, training these systems by reinforcement learning on users is not feasible. One solution is to train IR systems on user simulations that model the behavior of real users. ...
Some works propose to use US with attributes and characteristics (persona) [51, 52] to obtain both diversity and consistency in requests and dialogues. Li et al. ...
arXiv:2201.03435v1
fatcat:4yiydzysqzddvaju3jjoph7sdu
Ensemble-Based Deep Reinforcement Learning for Chatbots
2019
Neurocomputing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2019 pre-print arXiv:1908.10422v1 fatcat:uqtyhgy7rfbklmjlopjkvn3idm
Deep Reinforcement Learning (DRL) is promising for addressing this challenge, but its successful application remains an open question. ...
In addition to evaluations using held-out data, our results are further supported by a human evaluation that rated dialogues in terms of fluency, engagingness and consistency -- which revealed that our ...
A deep reinforcement learning agent approximates Q * using a multi-layer neural network [31] . ...
doi:10.1016/j.neucom.2019.08.007
fatcat:whpcdh5zxzdnlmwvrm6lru736u
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards
[article]
2019
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We train Deep Reinforcement Learning (DRL) agents using chitchat data in raw text---without any manual annotations. Experimental results using different splits of training data report the following. ...
Training chatbots using the reinforcement learning paradigm is challenging due to high-dimensional states, infinite action spaces and the difficulty in specifying the reward function. ...
High-level architecture of the proposed deep reinforcement learning approach for chatbots-see text for details provided labels), a Deep Reinforcement Learning (DRL) agent takes the role of one of the two ...
arXiv:1908.10331v1
fatcat:jtgyrwly4jfaxfeb7u5mbbxz7a
Deep Reinforcement Learning for Conversational AI
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
It is possible to scale deep reinforcement learning with the use of deep learning and do amazing tasks such as use of pixels in playing video games. ...
Various conversational models which are based on deep reinforcement learning (as well as deep learning) are also discussed. ...
Challenge 3: Coherent dialogue design e agent should generate consistent response while generating answer for semantically identical input. ...
arXiv:1709.05067v1
fatcat:ogg6iej4jjdflp2wcvpwxmfegi
« Previous
Showing results 1 — 15 out of 9,780 results