We introduce a new approach to generative data-driven dialogue systems (e.g. chatbots) called TransferTransfo which is a combination of a Transfer learning based training scheme and a high-capacity Transformer model. Fine-tuning is performed by using a multi-task objective which combines several unsupervised prediction tasks. The resulting fine-tuned model shows strong improvements over the current state-of-the-art end-to-end conversational models like memory augmented seq2seq and information-retrieval models. On the privately held PERSONA-CHAT dataset of the Conversational Intelligence Challenge 2, this approach obtains a new state-of-the-art, with respective perplexity, Hits@1 and F1 metrics of 16.28 (45 % absolute improvement), 80.7 (46 % absolute improvement) and 19.5 (20 % absolute improvement).
@article{arxiv.1901.08149,
title = {TransferTransfo: A Transfer Learning Approach for Neural Network Based Conversational Agents},
author = {Thomas Wolf and Victor Sanh and Julien Chaumond and Clement Delangue},
journal= {arXiv preprint arXiv:1901.08149},
year = {2019}
}
Comments
6 pages, 2 figures, 2 tables, NeurIPS 2018 CAI Workshop