Agree to Disagree: Improving Disagreement Detection with Dual GRUs
This work addresses the challenge of automating disagreement detection in online discussions, which is incremental as it builds on existing methods with specific improvements.
The paper tackled the problem of detecting agreement and disagreement in online discussions by introducing a Siamese-inspired architecture that eliminates the need for hand-crafted features, achieving a state-of-the-art average F1 score of 0.804 on the ABCD dataset.
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).