# Agree to Disagree: Improving Disagreement Detection with Dual GRUs

arXiv: https://arxiv.org/abs/1708.05582 · HTML: https://mlsanity.com/paper/1708.05582/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [STEM: Unsupervised STructural EMbedding for Stance Detection](https://mlsanity.com/paper/2112.00712/) (2021)
- [Addressing Data Scarcity in Multimodal User State Recognition by Combining Semi-Supervised and Supervised Learning](https://mlsanity.com/paper/2202.03775/) (2022)

## Abstract (arXiv)

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).