# Distant finetuning with discourse relations for stance classification

arXiv: https://arxiv.org/abs/2204.12693 · HTML: https://mlsanity.com/paper/2204.12693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 9 of 9)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations](https://mlsanity.com/paper/2010.03640/) (2020)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [Multilingual Argument Mining: Datasets and Analysis](https://mlsanity.com/paper/2010.06432/) (2020)
- [STANCY: Stance Classification Based on Consistency Cues](https://mlsanity.com/paper/1910.06048/) (2019)
- [Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention](https://mlsanity.com/paper/1801.03032/) (2018)

## Abstract (arXiv)

Approaches for the stance classification task, an important task for understanding argumentation in debates and detecting fake news, have been relying on models which deal with individual debate topics. In this paper, in order to train a system independent from topics, we propose a new method to extract data with silver labels from raw text to finetune a model for stance classification. The extraction relies on specific discourse relation information, which is shown as a reliable and accurate source for providing stance information. We also propose a 3-stage training framework where the noisy level in the data used for finetuning decreases over different stages going from the most noisy to the least noisy. Detailed experiments show that the automatically annotated dataset as well as the 3-stage training help improve model performance in stance classification. Our approach ranks 1st among 26 competing teams in the stance classification track of the NLPCC 2021 shared task Argumentative Text Understanding for AI Debater, which confirms the effectiveness of our approach.