Papers › AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained...

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

9 Feb 2021EMNLP (NLP4ConvAI) 2021 11arXiv:2102.05126archive 2025-07-28

Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, Ondřej Dušek

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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Code

ufal/augpt officialmentioned in paperpytorch report

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Tasks

Diversity End-To-End Dialogue Modelling Language Modeling Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
End-To-End Dialogue Modelling	MULTIWOZ 2.0	AuGPT	BLEU	17.2	#3 of 6	Archive leaderboard	report
End-To-End Dialogue Modelling	MULTIWOZ 2.0	AuGPT	MultiWOZ (Inform)	90.2	#3 of 6	Archive leaderboard	report
End-To-End Dialogue Modelling	MULTIWOZ 2.0	AuGPT	MultiWOZ (Success)	75.5	#3 of 6	Archive leaderboard	report
End-To-End Dialogue Modelling	MULTIWOZ 2.1	AuGPT	BLEU	17.2	#3 of 4	Archive leaderboard	report
End-To-End Dialogue Modelling	MULTIWOZ 2.1	AuGPT	MultiWOZ (Inform)	91.4	#3 of 4	Archive leaderboard	report
End-To-End Dialogue Modelling	MULTIWOZ 2.1	AuGPT	MultiWOZ (Success)	72.9	#3 of 4	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Adam Attention Attention Dropout BPE Cosine Annealing Dense Connections Discriminative Fine-Tuning Dropout GPT-2 Layer Normalization Linear Layer Linear Warmup With Cosine Annealing Multi-Head Attention Residual Connection Softmax Weight Decay

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