@inproceedings{duggenpudi-etal-2019-samvaadhana,
title = "{S}amvaadhana: A {T}elugu Dialogue System in Hospital Domain",
author = "Duggenpudi, Suma Reddy and
Siva Subrahamanyam Varma, Kusampudi and
Mamidi, Radhika",
editor = "Cherry, Colin and
Durrett, Greg and
Foster, George and
Haffari, Reza and
Khadivi, Shahram and
Peng, Nanyun and
Ren, Xiang and
Swayamdipta, Swabha",
booktitle = "Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-6126",
doi = "10.18653/v1/D19-6126",
pages = "234--242",
abstract = "In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.",
}
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<abstract>In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.</abstract>
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%0 Conference Proceedings
%T Samvaadhana: A Telugu Dialogue System in Hospital Domain
%A Duggenpudi, Suma Reddy
%A Siva Subrahamanyam Varma, Kusampudi
%A Mamidi, Radhika
%Y Cherry, Colin
%Y Durrett, Greg
%Y Foster, George
%Y Haffari, Reza
%Y Khadivi, Shahram
%Y Peng, Nanyun
%Y Ren, Xiang
%Y Swayamdipta, Swabha
%S Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F duggenpudi-etal-2019-samvaadhana
%X In this paper, a dialogue system for Hospital domain in Telugu, which is a resource-poor Dravidian language, has been built. It handles various hospital and doctor related queries. The main aim of this paper is to present an approach for modelling a dialogue system in a resource-poor language by combining linguistic and domain knowledge. Focusing on the question answering aspect of the dialogue system, we identified Question Classification and Query Processing as the two most important parts of the dialogue system. Our method combines deep learning techniques for question classification and computational rule-based analysis for query processing. Human evaluation of the system has been performed as there is no automated evaluation tool for dialogue systems in Telugu. Our system achieves a high overall rating along with a significantly accurate context-capturing method as shown in the results.
%R 10.18653/v1/D19-6126
%U https://aclanthology.org/D19-6126
%U https://doi.org/10.18653/v1/D19-6126
%P 234-242
Markdown (Informal)
[Samvaadhana: A Telugu Dialogue System in Hospital Domain](https://aclanthology.org/D19-6126) (Duggenpudi et al., 2019)
ACL
- Suma Reddy Duggenpudi, Kusampudi Siva Subrahamanyam Varma, and Radhika Mamidi. 2019. Samvaadhana: A Telugu Dialogue System in Hospital Domain. In Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019), pages 234–242, Hong Kong, China. Association for Computational Linguistics.