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A Conditional Variational Framework for Dialog Generation
[article]
2017
arXiv
pre-print
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In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. ...
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. ...
A snippet of the generated responses can be 4 Discussion and future work In this work, we propose a conditional variational framework for dialog generation and verify it on two scenarios. ...
arXiv:1705.00316v4
fatcat:72isabxomjdjjabbxjfgv66n3e
A Conditional Variational Framework for Dialog Generation
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
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In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. ...
Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. ...
Discussion and future work In this work, we propose a conditional variational framework for dialog generation and verify it on two scenarios. ...
doi:10.18653/v1/p17-2080
dblp:conf/acl/ShenSLLNZAL17
fatcat:wxej3r6euveupnea6ztckeqnx4
Task-Oriented Dialog Systems That Consider Multiple Appropriate Responses under the Same Context
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. ...
By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. ...
In another way, we are learning the correct dialog actions conditioned on a dialog state: L = t∈D log P (A t |S t ) (2) Due to the one-to-many property, for a specific dialog state S, there exists K different ...
doi:10.1609/aaai.v34i05.6507
fatcat:ofd3twwxt5h3pp4yahsn4lmzde
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
[article]
2019
arXiv
pre-print
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We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. ...
By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. ...
In another way, we are learning the correct dialog actions conditioned on a dialog state: L = t∈D log P (A t |S t ) (2) Due to the one-to-many property, for a specific dialog state S, there exists K different ...
arXiv:1911.10484v2
fatcat:24jjtd67xjg5jofmb4dspm4yku
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
[article]
2017
arXiv
pre-print
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Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures ...
We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. ...
Conditional Variational Autoencoder The variational autoencoder (VAE) (Kingma and Welling, 2013; Rezende et al., 2014) is one of the most popular frameworks for image generation. ...
arXiv:1703.10960v3
fatcat:eonwpha6rbfbnczrxw5kbg6zmi
A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning
[article]
2020
arXiv
pre-print
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Such latent variable modeling enables us to develop semi-supervised learning under the principled variational learning framework. ...
Structured belief states are crucial for user goal tracking and database query in task-oriented dialog systems. ...
Latent Belief State Dialog Models We first introduce LABES as a general dialog modeling framework in this section. ...
arXiv:2009.08115v3
fatcat:3rtcx454bje6ti747aucz72kde
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
2017
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
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Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures ...
We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. ...
Conditional Variational Autoencoder The variational autoencoder (VAE) (Kingma and Welling, 2013; Rezende et al., 2014) is one of the most popular frameworks for image generation. ...
doi:10.18653/v1/p17-1061
dblp:conf/acl/ZhaoZE17
fatcat:xk3tee3k6faddj2ous37qulwyq
A study of interaction between dialog and decision for human-robot collaborative task achievement
2007
RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication
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With this framework, we investigate various task negotiation situations for a social robot in a fetch-and-carry scenario. ...
For the technical realization of the framework, the interface specification between the dialog and the decision making systems is also presented. ...
In general, it is challenging for the dialog system to identify relationships between tasks. ...
doi:10.1109/roman.2007.4415214
dblp:conf/ro-man/ClodicAMLWS07
fatcat:utyqk3rawrcqfcx63cuom6ud7i
Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems
[article]
2020
arXiv
pre-print
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not generate any novel dialog flow variations. ...
50% relative accuracy improvements on a held-out test set compared to a baseline dialog generation approach that only samples natural language and entity value variations from existing catalogs but does ...
To address the time and cost requirements of WoZ setups, the authors in [12] proposed a Machines-Talking-To-Machines (M2M) framework, where a user and a system simulator interact to generate dialog outlines ...
arXiv:2011.08243v1
fatcat:7usbi43uqnccjnap3df2sykvdi
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
[article]
2019
arXiv
pre-print
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This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space ...
Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research. ...
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. ...
arXiv:1902.08858v2
fatcat:hkiixigjlzbq7njrmw3ztoohpa
Bootstrapping Dialog Models from Human to Human Conversation Logs
2021
AAAI Conference on Artificial Intelligence
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However, a dialog designer needs to rely on domain experts to manually build the dialog model -by creating dialog flow nodes and modeling user intents. ...
State-of-the-art commercial dialog platforms provide powerful tools to build a conversational agent. These platforms provide complete control to the dialog designer to model useragent interactions. ...
These frameworks enable the Dialog Designer to specify user intents and a dialog flow. ...
dblp:conf/aaai/DhooliaKCJ21
fatcat:ygwrhp7hjfh7fgyqcwpkgffcdu
Adversarial Mutual Information for Text Generation
[article]
2020
arXiv
pre-print
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to lead to a tighter lower bound of maximum mutual information for the variational information maximization problem. ...
In this paper, we propose Adversarial Mutual Information (AMI): a text generation framework which is formed as a novel saddle point (min-max) optimization aiming to identify joint interactions between ...
Dialog Generation We first verify the effectiveness of our method on the dialog generation task, which requires to generate a coherent and meaningful response given a conversation history. ...
arXiv:2007.00067v1
fatcat:athwkrd73vgxjmwzutdwtkhjra
Hierarchy Response Learning for Neural Conversation Generation
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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levels of diversity using the conditional variational autoencoders. ...
Specifically, a hierarchical response generation (HRG) framework is proposed to capture the conversation intention in a natural and coherent way. ...
Finally, an efficient training method is proposed to learn the model within the framework of conditional variational autoencoders (CVAE) (Doersch, 2016) . ...
doi:10.18653/v1/d19-1186
dblp:conf/emnlp/ZhangZ19
fatcat:kh2amcqnwbhbzd45wcbvaynf4e
Adversarial Learning on the Latent Space for Diverse Dialog Generation
[article]
2020
arXiv
pre-print
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In this paper, we propose a two-step framework based on generative adversarial nets for generating conditioned responses. ...
Generating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation but also being able to generate fluent sentences during inference. ...
A dialog generation system can be divided into two parts: 1) encoding the context of the conversation, and 2) generating a response conditioned on the given context. ...
arXiv:1911.03817v3
fatcat:bd5zbc35mnbldik3pbaho5nn5i
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
2019
Proceedings of the 2019 Conference of the North
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This paper proposes a novel latent action framework that treats the action spaces of an end-to-end dialog agent as latent variables and develops unsupervised methods in order to induce its own action space ...
Our detailed analysis also provides insights about various latent variable approaches for policy learning and can serve as a foundation for developing better latent actions in future research. 1 ...
Learning discourse-level diversity for neural dialog models using conditional variational autoencoders. ...
doi:10.18653/v1/n19-1123
dblp:conf/naacl/ZhaoXE19
fatcat:ykr5uujft5cznfikcno5wgyayy
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