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    <abstract>Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.</abstract>
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    <identifier type="doi">10.18653/v1/2020.findings-emnlp.81</identifier>
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%0 Conference Proceedings
%T Pretrained Language Models for Dialogue Generation with Multiple Input Sources
%A Cao, Yu
%A Bi, Wei
%A Fang, Meng
%A Tao, Dacheng
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F cao-etal-2020-pretrained
%X Large-scale pretrained language models have achieved outstanding performance on natural language understanding tasks. However, it is still under investigating how to apply them to dialogue generation tasks, especially those with responses conditioned on multiple sources. Previous work simply concatenates all input sources or averages information from different input sources. In this work, we study dialogue models with multiple input sources adapted from the pretrained language model GPT2. We explore various methods to fuse multiple separate attention information corresponding to different sources. Our experimental results show that proper fusion methods deliver higher relevance with dialogue history than simple fusion baselines.
%R 10.18653/v1/2020.findings-emnlp.81
%U https://aclanthology.org/2020.findings-emnlp.81
%U https://doi.org/10.18653/v1/2020.findings-emnlp.81
%P 909-917

Markdown (Informal)

[Pretrained Language Models for Dialogue Generation with Multiple Input Sources](https://aclanthology.org/2020.findings-emnlp.81) (Cao et al., Findings 2020)

• Pretrained Language Models for Dialogue Generation with Multiple Input Sources (Cao et al., Findings 2020)

ACL

• Yu Cao, Wei Bi, Meng Fang, and Dacheng Tao. 2020. Pretrained Language Models for Dialogue Generation with Multiple Input Sources. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 909–917, Online. Association for Computational Linguistics.