Deploying a Retrieval based Response Model for Task Oriented Dialogues
- URL: http://arxiv.org/abs/2210.14379v1
- Date: Tue, 25 Oct 2022 23:10:19 GMT
- Title: Deploying a Retrieval based Response Model for Task Oriented Dialogues
- Authors: Lahari Poddar, Gy\"orgy Szarvas, Cheng Wang, Jorge Balazs, Pavel
Danchenko and Patrick Ernst
- Abstract summary: Task-oriented dialogue systems need to have high conversational capability, be easily adaptable to changing situations and conform to business constraints.
This paper describes a 3-step procedure to develop a conversational model that satisfies these criteria and can efficiently scale to rank a large set of response candidates.
- Score: 8.671263996400844
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Task-oriented dialogue systems in industry settings need to have high
conversational capability, be easily adaptable to changing situations and
conform to business constraints. This paper describes a 3-step procedure to
develop a conversational model that satisfies these criteria and can
efficiently scale to rank a large set of response candidates. First, we provide
a simple algorithm to semi-automatically create a high-coverage template set
from historic conversations without any annotation. Second, we propose a neural
architecture that encodes the dialogue context and applicable business
constraints as profile features for ranking the next turn. Third, we describe a
two-stage learning strategy with self-supervised training, followed by
supervised fine-tuning on limited data collected through a human-in-the-loop
platform. Finally, we describe offline experiments and present results of
deploying our model with human-in-the-loop to converse with live customers
online.
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