Argumentative Link Prediction using Residual Networks and Multi-Objective Learning
We explore the use of residual networks for argumentation mining, with an emphasis on link prediction. The method we propose makes no assumptions on document or argument structure. We evaluate it on a challenging dataset consisting of user-generated comments collected from an online platform. Results show that our model outperforms an equivalent deep network and offers results comparable with state-of-the-art methods that rely on domain knowledge.
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Methods
Batch Normalization •
BiLSTM •
Dropout •
GloVe •
LSTM •
ReLU •
Residual Block •
Residual Connection •
ResNet •
Sigmoid Activation •
Tanh Activation
