We present a knowledge-grounded dialog system developed for the ninth Dialog System Technology Challenge (DSTC9) Track 1 - Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access. We leverage transfer learning with existing language models to accomplish the tasks in this challenge track. Specifically, we divided the task into four sub-tasks and fine-tuned several Transformer models on each of the sub-tasks. We made additional changes that yielded gains in both performance and efficiency, including the combination of the model with traditional entity-matching techniques, and the addition of a pointer network to the output layer of the language model.
@article{arxiv.2106.14444,
title = {A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models},
author = {Weijie Zhang and Jiaoxuan Chen and Haipang Wu and Sanhui Wan and Gongfeng Li},
journal= {arXiv preprint arXiv:2106.14444},
year = {2021}
}