@inproceedings{zheng-etal-2023-dialogqae,
title = "{D}ialog{QAE}: N-to-N Question Answer Pair Extraction from Customer Service Chatlog",
author = "Zheng, Xin and
Liu, Tianyu and
Meng, Haoran and
Wang, Xu and
Jiang, Yufan and
Rao, Mengliang and
Lin, Binghuai and
Cao, Yunbo and
Sui, Zhifang",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.435",
doi = "10.18653/v1/2023.findings-emnlp.435",
pages = "6540--6558",
abstract = "Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.",
}
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<abstract>Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.</abstract>
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%0 Conference Proceedings
%T DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog
%A Zheng, Xin
%A Liu, Tianyu
%A Meng, Haoran
%A Wang, Xu
%A Jiang, Yufan
%A Rao, Mengliang
%A Lin, Binghuai
%A Cao, Yunbo
%A Sui, Zhifang
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F zheng-etal-2023-dialogqae
%X Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.
%R 10.18653/v1/2023.findings-emnlp.435
%U https://aclanthology.org/2023.findings-emnlp.435
%U https://doi.org/10.18653/v1/2023.findings-emnlp.435
%P 6540-6558
Markdown (Informal)
[DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog](https://aclanthology.org/2023.findings-emnlp.435) (Zheng et al., Findings 2023)
ACL
- Xin Zheng, Tianyu Liu, Haoran Meng, Xu Wang, Yufan Jiang, Mengliang Rao, Binghuai Lin, Yunbo Cao, and Zhifang Sui. 2023. DialogQAE: N-to-N Question Answer Pair Extraction from Customer Service Chatlog. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6540–6558, Singapore. Association for Computational Linguistics.