  This paper presents models for detecting agreement/disagreement in online
discussions. In this work we show that by using a Siamese inspired architecture
to encode the discussions, we no longer need to rely on hand-crafted features
to exploit the meta thread structure. We evaluate our model on existing online
discussion corpora - ABCD, IAC and AWTP. Experimental results on ABCD dataset
show that by fusing lexical and word embedding features, our model achieves the
state of the art performance of 0.804 average F1 score. We also show that the
model trained on ABCD dataset performs competitively on relatively smaller
annotated datasets (IAC and AWTP).
