@inproceedings{yao-etal-2023-auxiliary,
title = "An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation",
author = "Yao, Yuanzhou and
Zhang, Zhao and
Yang, Kaijia and
Liang, Huasheng and
Yan, Qiang and
Xu, Yongjun",
editor = "Wang, Mingxuan and
Zitouni, Imed",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-industry.50",
doi = "10.18653/v1/2023.emnlp-industry.50",
pages = "522--531",
abstract = "Service accounts, including organizations{'} official accounts and mini-programs, provide various convenient services for users, and have become crucial components of a number of applications. Therefore, retrieving service accounts quickly and accurately is vital. However, this task suffers from the problem of limited human annotation, i.e., manually assessing account functionality and assigning ratings based on user experience is both labor-intensive and time-consuming. To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL). Specifically, the proposed method introduces multiple auxiliary tasks, which is able to utilized the log data from our application as supervision, and enhance the performance of the main task, service account retrieval. Furthermore, we introduce an Adaptive Hierarchical Fusion Module (AHF module) into our approach. This module is designed to adaptively perform hierarchical fusion of embeddings from auxiliary tasks into the main task, thereby enhancing the model efficacy. Experiments on two real-world industrial datasets demonstrate the effectiveness of our proposed approach.",
}
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<abstract>Service accounts, including organizations’ official accounts and mini-programs, provide various convenient services for users, and have become crucial components of a number of applications. Therefore, retrieving service accounts quickly and accurately is vital. However, this task suffers from the problem of limited human annotation, i.e., manually assessing account functionality and assigning ratings based on user experience is both labor-intensive and time-consuming. To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL). Specifically, the proposed method introduces multiple auxiliary tasks, which is able to utilized the log data from our application as supervision, and enhance the performance of the main task, service account retrieval. Furthermore, we introduce an Adaptive Hierarchical Fusion Module (AHF module) into our approach. This module is designed to adaptively perform hierarchical fusion of embeddings from auxiliary tasks into the main task, thereby enhancing the model efficacy. Experiments on two real-world industrial datasets demonstrate the effectiveness of our proposed approach.</abstract>
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%0 Conference Proceedings
%T An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation
%A Yao, Yuanzhou
%A Zhang, Zhao
%A Yang, Kaijia
%A Liang, Huasheng
%A Yan, Qiang
%A Xu, Yongjun
%Y Wang, Mingxuan
%Y Zitouni, Imed
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F yao-etal-2023-auxiliary
%X Service accounts, including organizations’ official accounts and mini-programs, provide various convenient services for users, and have become crucial components of a number of applications. Therefore, retrieving service accounts quickly and accurately is vital. However, this task suffers from the problem of limited human annotation, i.e., manually assessing account functionality and assigning ratings based on user experience is both labor-intensive and time-consuming. To this end, this paper proposes a novel approach, the Auxiliary task Boosted Multi-Task Learning method (AuxBoost-MTL). Specifically, the proposed method introduces multiple auxiliary tasks, which is able to utilized the log data from our application as supervision, and enhance the performance of the main task, service account retrieval. Furthermore, we introduce an Adaptive Hierarchical Fusion Module (AHF module) into our approach. This module is designed to adaptively perform hierarchical fusion of embeddings from auxiliary tasks into the main task, thereby enhancing the model efficacy. Experiments on two real-world industrial datasets demonstrate the effectiveness of our proposed approach.
%R 10.18653/v1/2023.emnlp-industry.50
%U https://aclanthology.org/2023.emnlp-industry.50
%U https://doi.org/10.18653/v1/2023.emnlp-industry.50
%P 522-531
Markdown (Informal)
[An Auxiliary Task Boosted Multi-task Learning Method for Service Account Retrieval with Limited Human Annotation](https://aclanthology.org/2023.emnlp-industry.50) (Yao et al., EMNLP 2023)
ACL