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Neural dialogue models, despite their successes, still suffer from lack ofrelevance, diversity, and in many cases coherence in their generated responses.These issues can attributed to reasons including (1) short-range modelarchitectures that capture limited temporal dependencies, (2) limitations ofthe maximum likelihood training objective, (3) the concave entropy profile ofdialogue datasets resulting in short and generic responses, and (4) theout-of-vocabulary problem leading to generation of a large number of <UNK>tokens. On the other hand, transformer-based models such as GPT-2 havedemonstrated an excellent ability to capture long-range structures in languagemodeling tasks. In this paper, we present DLGNet, a transformer-based model fordialogue modeling. We specifically examine the use of DLGNet for multi-turndialogue response generation. In our experiments, we evaluate DLGNet on theopen-domain Movie Triples dataset and the closed-domain Ubuntu Dialoguedataset. DLGNet models, although trained with only the maximum likelihoodobjective, achieve significant improvements over state-of-the-art multi-turndialogue models. They also produce best performance to date on the two datasetsbased on several metrics, including BLEU, ROUGE, and distinct n-gram. Ouranalysis shows that the performance improvement is mostly due to thecombination of (1) the long-range transformer architecture with (2) theinjection of random informative paddings. Other contributing factors includethe joint modeling of dialogue context and response, and the 100% tokenizationcoverage from the byte pair encoding (BPE).

DLGNet: A Transformer-based Model for Dialogue Response Generation