relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3171/
title: Large-scale Exploration of Neural Relation Classification Architectures
creator: Le, Hoang Quynh
creator: Can, Duy Cat
creator: Vu, Tien Sinh
creator: Dang, Thanh Hai
creator: Pilehvar, Mohammad Taher
creator: Collier, Nigel
subject: Information Technology (IT)
description: 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. Example data and source code are available at: https://github. com/aidantee/MASS.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
format: application/pdf
language: en
identifier: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/3171/1/D18-1250.pdf
identifier:   Le, Hoang Quynh and Can, Duy Cat and Vu, Tien Sinh and Dang, Thanh Hai and Pilehvar, Mohammad Taher and Collier, Nigel  (2018) Large-scale Exploration of Neural Relation Classification Architectures.  In: Conference on Empirical Methods in Natural Language Processing (EMNLP 2018), October 31 - November 4 2018, Brussels, Belgium.     
relation: http://www.aclweb.org/anthology/D18-1250