AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models

Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.

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Results from the Paper

Ranked #3 on End-To-End Dialogue Modelling on MULTIWOZ 2.0 (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
End-To-End Dialogue Modelling MULTIWOZ 2.0 AuGPT MultiWOZ (Success) 75.5 # 3
MultiWOZ (Inform) 90.2 # 2
BLEU 17.2 # 4
End-To-End Dialogue Modelling MULTIWOZ 2.1 AuGPT MultiWOZ (Success) 72.9 # 2
MultiWOZ (Inform) 91.4 # 2
BLEU 17.2 # 3