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We introduce a dialogue policy based on a transformer architecture, where the self-attention mechanism operates over the sequence of dialogue turns.
Attention, intentions, and the structure of discourse
Barbara J Grosz and Candace L Sidner · 1986
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Partially observable markov decision processes for spoken dialog systems
Jason D Williams and Steve Young · 2007
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The ravenclaw dialog management framework: Architecture and systems
Dan Bohus and Alexander I Rudnicky · 2009
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A hierarchical recurrent encoder-decoder for generative context-aware query suggestion
Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob Grue Simonsen, and Jian-Yun Nie · 2015
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WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang, Wen-tau Yih, and Christopher Meek · 2015
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Building end-to-end dialogue systems using generative hierarchical neural network models
Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau · 2016
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Learning end-to-end goal-oriented dialog
Antoine Bordes, Y-Lan Boureau, and Jason Weston · 2016
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The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems
Ryan Lowe, Nissan Pow, Iulian Serban, and Joelle Pineau · 2016
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Neural belief tracker: Data-driven dialogue state tracking
Nikola Mrkšić, Diarmuid O Séaghdha, Tsung-Hsien Wen, Blaise Thomson, and Steve Young · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Jason D Williams, Kavosh Asadi, and Geoffrey Zweig · 2017
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Rasa: Open source language understanding and dialogue management
Tom Bocklisch, Joey Faulkner, Nick Pawlowski, and Alan Nichol · 2017
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Starspace: Embed all the things!
Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, and Jason Weston · 2017
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Few-shot generalization across dialogue tasks
Vladimir Vlasov, Akela Drissner-Schmid, and Alan Nichol · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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On the robustness of self-attentive models
Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan, Wei Wei, Wen-Lian Hsu, and Cho-Jui Hsieh · 2019
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Training neural response selection for task-oriented dialogue systems
Matthew Henderson, Ivan Vulić, Daniela Gerz, Iñigo Casanueva, Paweł Budzianowski, Sam Coope, Georgios Spithourakis, Tsung-Hsien Wen, Nikola Mrkšić, and Pei-Hao Su · 2019
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Pretraining methods for dialog context representation learning
Shikib Mehri, Evgeniia Razumovsakaia, Tiancheng Zhao, and Maxine Eskenazi · 2019
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MetaLWOz, 2019
Hannes Schulz, Adam Atkinson, Mahmoud Adada, Kaheer Suleman, and Shikhar Sharma · 2019
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Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Wizard of wikipedia: Knowledge-powered conversational agents
Emily Dinan, Stephen Roller, Kurt Shuster, Angela Fan, Michael Auli, and Jason Weston · 2018
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Multiwoz-a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
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Modeling intent, dialog policies and response adaptation for goal-oriented interactions
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Transformer-xl: Attentive language models beyond a fixed-length context
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Mihail Eric, Rahul Goel, Shachi Paul, Abhishek Sethi, Sanchit Agarwal, Shuyag Gao, and Dilek Hakkani-Tur · 2019
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Taskmaster-1:Toward a realistic and diverse dialog dataset
Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Kyu-Young Kim, and Andy Cedilnik · 2019
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Multi-granularity representations of dialog
Shikib Mehri and Maxine Eskenazi · 2019
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Where is the context?–a critique of recent dialogue datasets
Johannes EM Mosig, Vladimir Vlasov, and Alan Nichol · 2020
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