@inproceedings{choi-etal-2018-self,
title = "Self-Learning Architecture for Natural Language Generation",
author = "Choi, Hyungtak and
K.M., Siddarth and
Yang, Haehun and
Jeon, Heesik and
Hwang, Inchul and
Kim, Jihie",
editor = "Krahmer, Emiel and
Gatt, Albert and
Goudbeek, Martijn",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
month = nov,
year = "2018",
address = "Tilburg University, The Netherlands",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6520/",
doi = "10.18653/v1/W18-6520",
pages = "165--170",
abstract = "In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions."
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<abstract>In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.</abstract>
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%0 Conference Proceedings
%T Self-Learning Architecture for Natural Language Generation
%A Choi, Hyungtak
%A K.M., Siddarth
%A Yang, Haehun
%A Jeon, Heesik
%A Hwang, Inchul
%A Kim, Jihie
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F choi-etal-2018-self
%X In this paper, we propose a self-learning architecture for generating natural language templates for conversational assistants. Generating templates to cover all the combinations of slots in an intent is time consuming and labor-intensive. We examine three different models based on our proposed architecture - Rule-based model, Sequence-to-Sequence (Seq2Seq) model and Semantically Conditioned LSTM (SC-LSTM) model for the IoT domain - to reduce the human labor required for template generation. We demonstrate the feasibility of template generation for the IoT domain using our self-learning architecture. In both automatic and human evaluation, the self-learning architecture outperforms previous works trained with a fully human-labeled dataset. This is promising for commercial conversational assistant solutions.
%R 10.18653/v1/W18-6520
%U https://aclanthology.org/W18-6520/
%U https://doi.org/10.18653/v1/W18-6520
%P 165-170
Markdown (Informal)
[Self-Learning Architecture for Natural Language Generation](https://aclanthology.org/W18-6520/) (Choi et al., INLG 2018)
ACL
- Hyungtak Choi, Siddarth K.M., Haehun Yang, Heesik Jeon, Inchul Hwang, and Jihie Kim. 2018. Self-Learning Architecture for Natural Language Generation. In Proceedings of the 11th International Conference on Natural Language Generation, pages 165–170, Tilburg University, The Netherlands. Association for Computational Linguistics.