@inproceedings{khasanova-etal-2022-developing,
title = "Developing a Production System for {P}urpose of {C}all Detection in Business Phone Conversations",
author = "Khasanova, Elena and
Hiranandani, Pooja and
Gardiner, Shayna and
Chen, Cheng and
Corston-Oliver, Simon and
Fu, Xue-Yong",
editor = "Loukina, Anastassia and
Gangadharaiah, Rashmi and
Min, Bonan",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-industry.29",
doi = "10.18653/v1/2022.naacl-industry.29",
pages = "259--267",
abstract = "For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.",
}
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<abstract>For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.</abstract>
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%0 Conference Proceedings
%T Developing a Production System for Purpose of Call Detection in Business Phone Conversations
%A Khasanova, Elena
%A Hiranandani, Pooja
%A Gardiner, Shayna
%A Chen, Cheng
%A Corston-Oliver, Simon
%A Fu, Xue-Yong
%Y Loukina, Anastassia
%Y Gangadharaiah, Rashmi
%Y Min, Bonan
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F khasanova-etal-2022-developing
%X For agents at a contact centre receiving calls, the most important piece of information is the reason for a given call. An agent cannot provide support on a call if they do not know why a customer is calling. In this paper we describe our implementation of a commercial system to detect Purpose of Call statements in English business call transcripts in real time. We present a detailed analysis of types of Purpose of Call statements and language patterns related to them, discuss an approach to collect rich training data by bootstrapping from a set of rules to a neural model, and describe a hybrid model which consists of a transformer-based classifier and a set of rules by leveraging insights from the analysis of call transcripts. The model achieved 88.6 F1 on average in various types of business calls when tested on real life data and has low inference time. We reflect on the challenges and design decisions when developing and deploying the system.
%R 10.18653/v1/2022.naacl-industry.29
%U https://aclanthology.org/2022.naacl-industry.29
%U https://doi.org/10.18653/v1/2022.naacl-industry.29
%P 259-267
Markdown (Informal)
[Developing a Production System for Purpose of Call Detection in Business Phone Conversations](https://aclanthology.org/2022.naacl-industry.29) (Khasanova et al., NAACL 2022)
ACL
- Elena Khasanova, Pooja Hiranandani, Shayna Gardiner, Cheng Chen, Simon Corston-Oliver, and Xue-Yong Fu. 2022. Developing a Production System for Purpose of Call Detection in Business Phone Conversations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track, pages 259–267, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.