Large-scale Exploration of Neural Relation Classification Architectures

Hoang-Quynh Le, Duy-Cat Can, Sinh T. Vu, Thanh Hai Dang, Mohammad Taher Pilehvar, Nigel Collier


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.
Anthology ID:
D18-1250
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2266–2277
Language:
URL:
https://aclanthology.org/D18-1250
DOI:
10.18653/v1/D18-1250
Bibkey:
Cite (ACL):
Hoang-Quynh Le, Duy-Cat Can, Sinh T. Vu, Thanh Hai Dang, Mohammad Taher Pilehvar, and Nigel Collier. 2018. Large-scale Exploration of Neural Relation Classification Architectures. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2266–2277, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Large-scale Exploration of Neural Relation Classification Architectures (Le et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1250.pdf
Attachment:
 D18-1250.Attachment.pdf
Code
 aidantee/MASS +  additional community code
Data
SemEval-2010 Task-8