@inproceedings{agarwal-etal-2018-knowledge,
title = "A Knowledge-Grounded Multimodal Search-Based Conversational Agent",
author = "Agarwal, Shubham and
Du{\v{s}}ek, Ond{\v{r}}ej and
Konstas, Ioannis and
Rieser, Verena",
editor = "Chuklin, Aleksandr and
Dalton, Jeff and
Kiseleva, Julia and
Borisov, Alexey and
Burtsev, Mikhail",
booktitle = "Proceedings of the 2018 {EMNLP} Workshop {SCAI}: The 2nd International Workshop on Search-Oriented Conversational {AI}",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-5709",
doi = "10.18653/v1/W18-5709",
pages = "59--66",
abstract = "Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).",
}
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<abstract>Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).</abstract>
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%0 Conference Proceedings
%T A Knowledge-Grounded Multimodal Search-Based Conversational Agent
%A Agarwal, Shubham
%A Dušek, Ondřej
%A Konstas, Ioannis
%A Rieser, Verena
%Y Chuklin, Aleksandr
%Y Dalton, Jeff
%Y Kiseleva, Julia
%Y Borisov, Alexey
%Y Burtsev, Mikhail
%S Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F agarwal-etal-2018-knowledge
%X Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).
%R 10.18653/v1/W18-5709
%U https://aclanthology.org/W18-5709
%U https://doi.org/10.18653/v1/W18-5709
%P 59-66
Markdown (Informal)
[A Knowledge-Grounded Multimodal Search-Based Conversational Agent](https://aclanthology.org/W18-5709) (Agarwal et al., EMNLP 2018)
ACL
- Shubham Agarwal, Ondřej Dušek, Ioannis Konstas, and Verena Rieser. 2018. A Knowledge-Grounded Multimodal Search-Based Conversational Agent. In Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI, pages 59–66, Brussels, Belgium. Association for Computational Linguistics.