@inproceedings{huang-etal-2023-ensemble,
title = "Ensemble Method via Ranking Model for Conversational Modeling with Subjective Knowledge",
author = "Huang, Xin and
Min Tan, Kye and
Duan, Richeng and
Zou, Bowei",
editor = "Chen, Yun-Nung and
Crook, Paul and
Galley, Michel and
Ghazarian, Sarik and
Gunasekara, Chulaka and
Gupta, Raghav and
Hedayatnia, Behnam and
Kottur, Satwik and
Moon, Seungwhan and
Zhang, Chen",
booktitle = "Proceedings of The Eleventh Dialog System Technology Challenge",
month = sep,
year = "2023",
address = "Prague, Czech Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.dstc-1.20",
pages = "177--184",
abstract = "This paper describes our submission to the fifth track of the 11th Dialog System Technology Challenge (DSTC-11), which focuses on {``}Task-oriented Conversational Modeling with Subjective Knowledge{''}. We focus on response generation and leverage a ranking strategy to ensemble individual models of BART, Long-T5, and a fine-tuned large language model based on LLaMA. The strategy is supplemented by other techniques like low rank adaptation to maintain efficient utilization of these large models while still achieving optimal performance. The experiments show that the ensemble method outperforms individual models and the baseline method. Our model was ranked 1st place in ROUGE{\_}1, 2nd place in ROUGE{\_}L score and 4th place in human evaluation among a total of 14 participating teams.",
}
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<abstract>This paper describes our submission to the fifth track of the 11th Dialog System Technology Challenge (DSTC-11), which focuses on “Task-oriented Conversational Modeling with Subjective Knowledge”. We focus on response generation and leverage a ranking strategy to ensemble individual models of BART, Long-T5, and a fine-tuned large language model based on LLaMA. The strategy is supplemented by other techniques like low rank adaptation to maintain efficient utilization of these large models while still achieving optimal performance. The experiments show that the ensemble method outperforms individual models and the baseline method. Our model was ranked 1st place in ROUGE_1, 2nd place in ROUGE_L score and 4th place in human evaluation among a total of 14 participating teams.</abstract>
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%0 Conference Proceedings
%T Ensemble Method via Ranking Model for Conversational Modeling with Subjective Knowledge
%A Huang, Xin
%A Min Tan, Kye
%A Duan, Richeng
%A Zou, Bowei
%Y Chen, Yun-Nung
%Y Crook, Paul
%Y Galley, Michel
%Y Ghazarian, Sarik
%Y Gunasekara, Chulaka
%Y Gupta, Raghav
%Y Hedayatnia, Behnam
%Y Kottur, Satwik
%Y Moon, Seungwhan
%Y Zhang, Chen
%S Proceedings of The Eleventh Dialog System Technology Challenge
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czech Republic
%F huang-etal-2023-ensemble
%X This paper describes our submission to the fifth track of the 11th Dialog System Technology Challenge (DSTC-11), which focuses on “Task-oriented Conversational Modeling with Subjective Knowledge”. We focus on response generation and leverage a ranking strategy to ensemble individual models of BART, Long-T5, and a fine-tuned large language model based on LLaMA. The strategy is supplemented by other techniques like low rank adaptation to maintain efficient utilization of these large models while still achieving optimal performance. The experiments show that the ensemble method outperforms individual models and the baseline method. Our model was ranked 1st place in ROUGE_1, 2nd place in ROUGE_L score and 4th place in human evaluation among a total of 14 participating teams.
%U https://aclanthology.org/2023.dstc-1.20
%P 177-184
Markdown (Informal)
[Ensemble Method via Ranking Model for Conversational Modeling with Subjective Knowledge](https://aclanthology.org/2023.dstc-1.20) (Huang et al., DSTC-WS 2023)
ACL