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        <title>Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling</title>
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    <abstract>Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and the rest of the data is automatically annotated. We follow that approach, by automatically constructing a large-scale weakly-labeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models. The proposed framework adapts the parameter updates to the models according to the estimated label-quality. On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of the training data is used.</abstract>
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    <identifier type="doi">10.18653/v1/2021.eacl-main.69</identifier>
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%0 Conference Proceedings
%T Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling
%A Chang, Ernie
%A Demberg, Vera
%A Marin, Alex
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F chang-etal-2021-jointly
%X Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and the rest of the data is automatically annotated. We follow that approach, by automatically constructing a large-scale weakly-labeled data with a fine-tuned GPT-2, and employ a semi-supervised framework to jointly train the NLG and NLU models. The proposed framework adapts the parameter updates to the models according to the estimated label-quality. On both the E2E and Weather benchmarks, we show that this weakly supervised training paradigm is an effective approach under low resource scenarios with as little as 10 data instances, and outperforming benchmark systems on both datasets when 100% of the training data is used.
%R 10.18653/v1/2021.eacl-main.69
%U https://aclanthology.org/2021.eacl-main.69
%U https://doi.org/10.18653/v1/2021.eacl-main.69
%P 818-829

Markdown (Informal)

[Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling](https://aclanthology.org/2021.eacl-main.69) (Chang et al., EACL 2021)

• Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling (Chang et al., EACL 2021)

ACL

• Ernie Chang, Vera Demberg, and Alex Marin. 2021. Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 818–829, Online. Association for Computational Linguistics.