@inproceedings{alavian-etal-2022-docalog,
title = "Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval",
author = "Alavian, Sayed Hesam and
Satvaty, Ali and
Sabouri, Sadra and
Asgari, Ehsaneddin and
Sameti, Hossein",
editor = "Feng, Song and
Wan, Hui and
Yuan, Caixia and
Yu, Han",
booktitle = "Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.dialdoc-1.16",
doi = "10.18653/v1/2022.dialdoc-1.16",
pages = "142--147",
abstract = "Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users{'} needs. This paper discusses our proposed approach, $Docalog$, for the DialDoc-22 (MultiDoc2Dial) shared task. $Docalog$ identifies the most relevant knowledge in the associated document, in a multi-document setting. $Docalog$, is a three-stage pipeline consisting of \textit{(1) a document retriever model (DR. TEIT)}, \textit{(2) an answer span prediction model}, and \textit{(3) an ultimate span picker} deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, $Docalog$ achieved f1-scores of 36.07{\%} and 28.44{\%} and SacreBLEU scores of 23.70{\%} and 20.52{\%}, respectively on the \textit{MDD-SEEN} and \textit{MDD-UNSEEN} folds.",
}
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<abstract>Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.</abstract>
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%0 Conference Proceedings
%T Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval
%A Alavian, Sayed Hesam
%A Satvaty, Ali
%A Sabouri, Sadra
%A Asgari, Ehsaneddin
%A Sameti, Hossein
%Y Feng, Song
%Y Wan, Hui
%Y Yuan, Caixia
%Y Yu, Han
%S Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F alavian-etal-2022-docalog
%X Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative answers based on users’ needs. This paper discusses our proposed approach, Docalog, for the DialDoc-22 (MultiDoc2Dial) shared task. Docalog identifies the most relevant knowledge in the associated document, in a multi-document setting. Docalog, is a three-stage pipeline consisting of (1) a document retriever model (DR. TEIT), (2) an answer span prediction model, and (3) an ultimate span picker deciding on the most likely answer span, out of all predicted spans. In the test phase of MultiDoc2Dial 2022, Docalog achieved f1-scores of 36.07% and 28.44% and SacreBLEU scores of 23.70% and 20.52%, respectively on the MDD-SEEN and MDD-UNSEEN folds.
%R 10.18653/v1/2022.dialdoc-1.16
%U https://aclanthology.org/2022.dialdoc-1.16
%U https://doi.org/10.18653/v1/2022.dialdoc-1.16
%P 142-147
Markdown (Informal)
[Docalog: Multi-document Dialogue System using Transformer-based Span Retrieval](https://aclanthology.org/2022.dialdoc-1.16) (Alavian et al., dialdoc 2022)
ACL