A comprehensive solution to retrieval-based chatbot construction

by   Kristen Moore, et al.

In this paper we present the results of our experiments in training and deploying a self-supervised retrieval-based chatbot trained with contrastive learning for assisting customer support agents. In contrast to most existing research papers in this area where the focus is on solving just one component of a deployable chatbot, we present an end-to-end set of solutions to take the reader from an unlabelled chatlogs to a deployed chatbot. This set of solutions includes creating a self-supervised dataset and a weakly labelled dataset from chatlogs, as well as a systematic approach to selecting a fixed list of canned responses. We present a hierarchical-based RNN architecture for the response selection model, chosen for its ability to cache intermediate utterance embeddings, which helped to meet deployment inference speed requirements. We compare the performance of this architecture across 3 different learning objectives: self-supervised contrastive learning, binary classification, and multi-class classification. We find that using a self-supervised contrastive learning model outperforms training the binary and multi-class classification models on a weakly labelled dataset. Our results validate that the self-supervised contrastive learning approach can be effectively used for a real-world chatbot scenario.


page 1

page 2

page 3

page 4


Multi-network Contrastive Learning Based on Global and Local Representations

The popularity of self-supervised learning has made it possible to train...

Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning

In this study, we investigate self-supervised representation learning fo...

Don't freeze: Finetune encoders for better Self-Supervised HAR

Recently self-supervised learning has been proposed in the field of huma...

ICLEA: Interactive Contrastive Learning for Self-supervised Entity Alignment

Self-supervised entity alignment (EA) aims to link equivalent entities a...

Video-based Contrastive Learning on Decision Trees: from Action Recognition to Autism Diagnosis

How can we teach a computer to recognize 10,000 different actions? Deep ...

Addressing Leakage in Self-Supervised Contextualized Code Retrieval

We address contextualized code retrieval, the search for code snippets h...

Self-supervised Contrastive Learning for Volcanic Unrest Detection

Ground deformation measured from Interferometric Synthetic Aperture Rada...

Please sign up or login with your details

Forgot password? Click here to reset