@inproceedings{yang-etal-2019-enhancing-pre,
title = "Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension",
author = "Yang, An and
Wang, Quan and
Liu, Jing and
Liu, Kai and
Lyu, Yajuan and
Wu, Hua and
She, Qiaoqiao and
Li, Sujian",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1226",
doi = "10.18653/v1/P19-1226",
pages = "2346--2357",
abstract = "Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).",
}
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<abstract>Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).</abstract>
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%0 Conference Proceedings
%T Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension
%A Yang, An
%A Wang, Quan
%A Liu, Jing
%A Liu, Kai
%A Lyu, Yajuan
%A Wu, Hua
%A She, Qiaoqiao
%A Li, Sujian
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F yang-etal-2019-enhancing-pre
%X Machine reading comprehension (MRC) is a crucial and challenging task in NLP. Recently, pre-trained language models (LMs), especially BERT, have achieved remarkable success, presenting new state-of-the-art results in MRC. In this work, we investigate the potential of leveraging external knowledge bases (KBs) to further improve BERT for MRC. We introduce KT-NET, which employs an attention mechanism to adaptively select desired knowledge from KBs, and then fuses selected knowledge with BERT to enable context- and knowledge-aware predictions. We believe this would combine the merits of both deep LMs and curated KBs towards better MRC. Experimental results indicate that KT-NET offers significant and consistent improvements over BERT, outperforming competitive baselines on ReCoRD and SQuAD1.1 benchmarks. Notably, it ranks the 1st place on the ReCoRD leaderboard, and is also the best single model on the SQuAD1.1 leaderboard at the time of submission (March 4th, 2019).
%R 10.18653/v1/P19-1226
%U https://aclanthology.org/P19-1226
%U https://doi.org/10.18653/v1/P19-1226
%P 2346-2357
Markdown (Informal)
[Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension](https://aclanthology.org/P19-1226) (Yang et al., ACL 2019)
ACL
- An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, and Sujian Li. 2019. Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2346–2357, Florence, Italy. Association for Computational Linguistics.