Large-scale Exploration of Neural Relation Classification Architectures
Hoang-Quynh Le, Duy-Cat Can, Tien-Sinh Vu, Thanh Hai Dang, Mohammad Taher Pilehvar, Nigel Collier · 2018
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.