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    <abstract>This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.</abstract>
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%0 Conference Proceedings
%T Encoding Explanatory Knowledge for Zero-shot Science Question Answering
%A Zhou, Zili
%A Valentino, Marco
%A Landers, Donal
%A Freitas, André
%Y Zarrieß, Sina
%Y Bos, Johan
%Y van Noord, Rik
%Y Abzianidze, Lasha
%S Proceedings of the 14th International Conference on Computational Semantics (IWCS)
%D 2021
%8 June
%I Association for Computational Linguistics
%C Groningen, The Netherlands (online)
%F zhou-etal-2021-encoding
%X This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.
%U https://aclanthology.org/2021.iwcs-1.5
%P 38-50

Markdown (Informal)

[Encoding Explanatory Knowledge for Zero-shot Science Question Answering](https://aclanthology.org/2021.iwcs-1.5) (Zhou et al., IWCS 2021)

• Encoding Explanatory Knowledge for Zero-shot Science Question Answering (Zhou et al., IWCS 2021)

ACL

• Zili Zhou, Marco Valentino, Donal Landers, and André Freitas. 2021. Encoding Explanatory Knowledge for Zero-shot Science Question Answering. In Proceedings of the 14th International Conference on Computational Semantics (IWCS), pages 38–50, Groningen, The Netherlands (online). Association for Computational Linguistics.