@inproceedings{le-etal-2018-large,
title = "Large-scale Exploration of Neural Relation Classification Architectures",
author = "Le, Hoang-Quynh and
Can, Duy-Cat and
Vu, Sinh T. and
Dang, Thanh Hai and
Pilehvar, Mohammad Taher and
Collier, Nigel",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1250",
doi = "10.18653/v1/D18-1250",
pages = "2266--2277",
abstract = "Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our {`}Man for All Seasons{'} approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.",
}
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<abstract>Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our ‘Man for All Seasons’ approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.</abstract>
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%0 Conference Proceedings
%T Large-scale Exploration of Neural Relation Classification Architectures
%A Le, Hoang-Quynh
%A Can, Duy-Cat
%A Vu, Sinh T.
%A Dang, Thanh Hai
%A Pilehvar, Mohammad Taher
%A Collier, Nigel
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F le-etal-2018-large
%X Experimental performance on the task of relation classification has generally improved using deep neural network architectures. One major drawback of reported studies is that individual models have been evaluated on a very narrow range of datasets, raising questions about the adaptability of the architectures, while making comparisons between approaches difficult. In this work, we present a systematic large-scale analysis of neural relation classification architectures on six benchmark datasets with widely varying characteristics. We propose a novel multi-channel LSTM model combined with a CNN that takes advantage of all currently popular linguistic and architectural features. Our ‘Man for All Seasons’ approach achieves state-of-the-art performance on two datasets. More importantly, in our view, the model allowed us to obtain direct insights into the continued challenges faced by neural language models on this task.
%R 10.18653/v1/D18-1250
%U https://aclanthology.org/D18-1250
%U https://doi.org/10.18653/v1/D18-1250
%P 2266-2277
Markdown (Informal)
[Large-scale Exploration of Neural Relation Classification Architectures](https://aclanthology.org/D18-1250) (Le et al., EMNLP 2018)
ACL