@inproceedings{verga-etal-2021-adaptable,
title = "Adaptable and Interpretable Neural {M}emory{O}ver Symbolic Knowledge",
author = "Verga, Pat and
Sun, Haitian and
Baldini Soares, Livio and
Cohen, William",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.288",
doi = "10.18653/v1/2021.naacl-main.288",
pages = "3678--3691",
abstract = "Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a {``}fact memory{''}. Each element of the fact memory is formed from a triple of vectors, where each vector corresponds to a KB entity or relation. Our LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model, despite using 5{\%} of the parameters. Most interestingly, we demonstrate that the model can be modified, without \textit{any} re-training, by updating the fact memory.",
}
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<abstract>Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a “fact memory”. Each element of the fact memory is formed from a triple of vectors, where each vector corresponds to a KB entity or relation. Our LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model, despite using 5% of the parameters. Most interestingly, we demonstrate that the model can be modified, without any re-training, by updating the fact memory.</abstract>
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%0 Conference Proceedings
%T Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge
%A Verga, Pat
%A Sun, Haitian
%A Baldini Soares, Livio
%A Cohen, William
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F verga-etal-2021-adaptable
%X Past research has demonstrated that large neural language models (LMs) encode surprising amounts of factual information: however, augmenting or modifying this information requires modifying a corpus and retraining, which is computationally expensive. To address this problem, we develop a neural LM that includes an interpretable neuro-symbolic KB in the form of a “fact memory”. Each element of the fact memory is formed from a triple of vectors, where each vector corresponds to a KB entity or relation. Our LM improves performance on knowledge-intensive question-answering tasks, sometimes dramatically, including a 27 point increase in one setting of WebQuestionsSP over a state-of-the-art open-book model, despite using 5% of the parameters. Most interestingly, we demonstrate that the model can be modified, without any re-training, by updating the fact memory.
%R 10.18653/v1/2021.naacl-main.288
%U https://aclanthology.org/2021.naacl-main.288
%U https://doi.org/10.18653/v1/2021.naacl-main.288
%P 3678-3691
Markdown (Informal)
[Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge](https://aclanthology.org/2021.naacl-main.288) (Verga et al., NAACL 2021)
ACL
- Pat Verga, Haitian Sun, Livio Baldini Soares, and William Cohen. 2021. Adaptable and Interpretable Neural MemoryOver Symbolic Knowledge. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3678–3691, Online. Association for Computational Linguistics.