@inproceedings{guo-etal-2020-evidence,
title = "Evidence-Aware Inferential Text Generation with Vector Quantised Variational {A}uto{E}ncoder",
author = "Guo, Daya and
Tang, Duyu and
Duan, Nan and
Yin, Jian and
Jiang, Daxin and
Zhou, Ming",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.544",
doi = "10.18653/v1/2020.acl-main.544",
pages = "6118--6129",
abstract = "Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoderdecoder manner and is equipped with Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2mind and Atomic datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.",
}
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%0 Conference Proceedings
%T Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder
%A Guo, Daya
%A Tang, Duyu
%A Duan, Nan
%A Yin, Jian
%A Jiang, Daxin
%A Zhou, Ming
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F guo-etal-2020-evidence
%X Generating inferential texts about an event in different perspectives requires reasoning over different contexts that the event occurs. Existing works usually ignore the context that is not explicitly provided, resulting in a context-independent semantic representation that struggles to support the generation. To address this, we propose an approach that automatically finds evidence for an event from a large text corpus, and leverages the evidence to guide the generation of inferential texts. Our approach works in an encoderdecoder manner and is equipped with Vector Quantised-Variational Autoencoder, where the encoder outputs representations from a distribution over discrete variables. Such discrete representations enable automatically selecting relevant evidence, which not only facilitates evidence-aware generation, but also provides a natural way to uncover rationales behind the generation. Our approach provides state-of-the-art performance on both Event2mind and Atomic datasets. More importantly, we find that with discrete representations, our model selectively uses evidence to generate different inferential texts.
%R 10.18653/v1/2020.acl-main.544
%U https://aclanthology.org/2020.acl-main.544
%U https://doi.org/10.18653/v1/2020.acl-main.544
%P 6118-6129
Markdown (Informal)
[Evidence-Aware Inferential Text Generation with Vector Quantised Variational AutoEncoder](https://aclanthology.org/2020.acl-main.544) (Guo et al., ACL 2020)
ACL