@inproceedings{burtsev-etal-2018-deeppavlov,
title = "{D}eep{P}avlov: Open-Source Library for Dialogue Systems",
author = "Burtsev, Mikhail and
Seliverstov, Alexander and
Airapetyan, Rafael and
Arkhipov, Mikhail and
Baymurzina, Dilyara and
Bushkov, Nickolay and
Gureenkova, Olga and
Khakhulin, Taras and
Kuratov, Yuri and
Kuznetsov, Denis and
Litinsky, Alexey and
Logacheva, Varvara and
Lymar, Alexey and
Malykh, Valentin and
Petrov, Maxim and
Polulyakh, Vadim and
Pugachev, Leonid and
Sorokin, Alexey and
Vikhreva, Maria and
Zaynutdinov, Marat",
editor = "Liu, Fei and
Solorio, Thamar",
booktitle = "Proceedings of {ACL} 2018, System Demonstrations",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-4021",
doi = "10.18653/v1/P18-4021",
pages = "122--127",
abstract = "Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.",
}
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<abstract>Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.</abstract>
<identifier type="citekey">burtsev-etal-2018-deeppavlov</identifier>
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<url>https://aclanthology.org/P18-4021</url>
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<date>2018-07</date>
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<start>122</start>
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%0 Conference Proceedings
%T DeepPavlov: Open-Source Library for Dialogue Systems
%A Burtsev, Mikhail
%A Seliverstov, Alexander
%A Airapetyan, Rafael
%A Arkhipov, Mikhail
%A Baymurzina, Dilyara
%A Bushkov, Nickolay
%A Gureenkova, Olga
%A Khakhulin, Taras
%A Kuratov, Yuri
%A Kuznetsov, Denis
%A Litinsky, Alexey
%A Logacheva, Varvara
%A Lymar, Alexey
%A Malykh, Valentin
%A Petrov, Maxim
%A Polulyakh, Vadim
%A Pugachev, Leonid
%A Sorokin, Alexey
%A Vikhreva, Maria
%A Zaynutdinov, Marat
%Y Liu, Fei
%Y Solorio, Thamar
%S Proceedings of ACL 2018, System Demonstrations
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F burtsev-etal-2018-deeppavlov
%X Adoption of messaging communication and voice assistants has grown rapidly in the last years. This creates a demand for tools that speed up prototyping of feature-rich dialogue systems. An open-source library DeepPavlov is tailored for development of conversational agents. The library prioritises efficiency, modularity, and extensibility with the goal to make it easier to develop dialogue systems from scratch and with limited data available. It supports modular as well as end-to-end approaches to implementation of conversational agents. Conversational agent consists of skills and every skill can be decomposed into components. Components are usually models which solve typical NLP tasks such as intent classification, named entity recognition or pre-trained word vectors. Sequence-to-sequence chit-chat skill, question answering skill or task-oriented skill can be assembled from components provided in the library.
%R 10.18653/v1/P18-4021
%U https://aclanthology.org/P18-4021
%U https://doi.org/10.18653/v1/P18-4021
%P 122-127
Markdown (Informal)
[DeepPavlov: Open-Source Library for Dialogue Systems](https://aclanthology.org/P18-4021) (Burtsev et al., ACL 2018)
ACL
- Mikhail Burtsev, Alexander Seliverstov, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurzina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, Alexey Litinsky, Varvara Logacheva, Alexey Lymar, Valentin Malykh, Maxim Petrov, Vadim Polulyakh, Leonid Pugachev, Alexey Sorokin, Maria Vikhreva, et al.. 2018. DeepPavlov: Open-Source Library for Dialogue Systems. In Proceedings of ACL 2018, System Demonstrations, pages 122–127, Melbourne, Australia. Association for Computational Linguistics.